Effects of Harvest and Climate Change on Polar Marine Ecosystems

Eﬀects of Harvest and Climate Change on Polar Marine Ecosystems Case Studies from the Antarctic Peninsula and Hudson Bay

Carie Hoover

B.Sc., The University of California Santa Barbara, 2002 M.Res., The University of St Andrews, 2005

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

The Faculty of Graduate Studies

(Resource Management and Environmental Studies)

THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2012 ⃝c Carie Hoover 2012 Abstract

This thesis applies food web modelling to increase our understanding of how the interaction of climate change and exploitation have historically altered, and continue to alter, marine polar ecosystems. Understanding stressors responsible for ecosystem level changes is important not only to the people and industries reliant on the resources, but for managers to make future decisions on resource uses. The first two chapters develop models of Hud- son Bay (Arctic) and Antarctic Peninsula (Antarctic) marine ecosystems, focused on re-creating changes in the past 30 years. Both ecosystems have undergone changes due to environmental factors, which are incorporated into the models. While the Hudson Bay model exhibits a shift from benthic to pelagic species, the Antarctic Peninsula model is identified to have more uniform declines across all species, as the main trophic link in the ecosystem, Antarctic krill declines. Model simulations are continued in the next two chapters, whereby future environmental changes are tested in conjunction with multiple exploitation levels. For Hudson Bay, continued harvest of marine mammals at current conditions results in large-scale declines for some species (narwhal, eastern Hudson Bay beluga, polar bears, and walrus), indicating current harvest levels are too high to sustain long term. Further shifts from benthic to pelagic species in the lower trophic levels favor fish species such as capelin and sandlance. Future simulations of the Antarctic Peninsula identify large reductions in ecosystem biomass of all species due changes in environmental conditions and an overall reduction in krill, with minimal ecosystem impacts from harvest. In the last chapter, an economic model is constructed to assess the use value of hunting narwhal and beluga in the Hudson Bay region. The economic impact to northern residents is considered as future model simulations of Hudson Bay reveal that these species may be susceptible to population declines, and issues of food security are becoming increasingly important. Economic analysis reveals the moti- vation to hunt in Hudson Bay may not be economically-driven, there are substantial benefits derived by northern communities through narwhal and beluga hunts. Results for each ecosystem are discussed as they pertain to future research and management of each ecosystem.

ii Preface

A version of chapter 2, co-authored with Tony Pitcher and Villy Christensen, has been re-submitted with revisions to Ecological Modelling. I constructed the model and wrote the manuscript. Villy Christensen was key in the development of the model structure, ﬁtting of the model, and other technical model aspects. Tony Pitcher provided guidance on model construction and discussions on the direction of the manuscript.

A version of chapter 4, co-authored with Tony Pitcher and Villy Christensen, has been re-submitted with revisions to Ecological Modelling. I created the model simulations and wrote the manuscript. In addition to their assistance with chapter 2 which utilizes the same model, both Villy Christensen and Tony Pitcher provided guidance with the future simulations. Villy Chris- tensen also provided technical model assistance.

A version of chapter 6, co-authored with Megan Bailey, Jeff Higdon, Steve Ferguson, and Rashid Sumaila, has been re-submitted with revisions to the journal Arctic. I conceptualized and constructed the model in addition to writing the manuscript. Megan Bailey assisted in model construction. Rashid Sumaila and Megan Bailey provided the framework for the model and assistance on economic analyses. Jeff Higdon and Steve Ferguson provided expertise on model parameters, in addition to Jeff Higdon collecting input parameter values during fieldwork in the north. All authors provided feedback on the submitted manuscript.

Chapters 3 and 5 are co-authored with Tony Pitcher and Evgeny Pakhomov, and will be submitted to a peer-review journal. I constructed the model for chapter 3, created the model simulations for chapter 5, and wrote both manuscripts. Tony Pitcher provided the idea for the model and guidance throughout the model construction, and provided guidance with model simulations. Evgeny Pakhomov was key in providing expertise to the ecology of the model which was important to the ﬁtting process, and contributed to the ecological relevance of the model.

iii Table of Contents

Abstract ...... ii

Preface ...... iii

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... ix

Acknowledgements ...... xii

Dedication ...... xiv

1 Introduction ...... 1 1.1 Ecosystem-Based Management ...... 1 1.2 Study Areas ...... 3 1.3 Ecopath with Ecosim ...... 10 1.4 Thesis Outline ...... 11

2 Impacts of Hunting, Fishing, and Climate Change to the Hudson Bay Marine Ecosystem 1970-2009 ...... 15 2.1 Synopsis ...... 15 2.2 Introduction ...... 16 2.3 Methods ...... 18 2.4 Results ...... 27 2.5 Discussion ...... 41

3 Eﬀects of Harvest and Climate Change on the Antarctic Peninsula Marine Ecosystem (FAO area 48.1) ...... 47 3.1 Synopsis ...... 47 3.2 Introduction ...... 48 3.3 Methods ...... 51

iv Table of Contents

3.4 Results ...... 60 3.5 Discussion ...... 73

4 Future Impacts of Hunting, Fishing, and Climate Change on the Hudson Bay Marine Ecosystem ...... 80 4.1 Synopsis ...... 80 4.2 Introduction ...... 81 4.3 Methods ...... 84 4.4 Results ...... 91 4.5 Discussion ...... 109 4.6 Hudson Bay Biomass and Morality Figures ...... 116

5 Future Impacts of Fishing and Climate Change on the Antarc- tic Peninsula Marine Ecosystem ...... 121 5.1 Synopsis ...... 121 5.2 Introduction ...... 122 5.3 Methods ...... 124 5.4 Results ...... 130 5.5 Discussion ...... 158 5.6 Antarctic Peninsula Biomass and Mortality Figures . . . . . 165

6 Estimating the Economic Value of Narwhal and Beluga Hunts in Hudson Bay, Nunavut ...... 172 6.1 Synopsis ...... 172 6.2 Introduction ...... 173 6.3 Methods ...... 176 6.4 Results ...... 196 6.5 Discussion ...... 202

7 Conclusions ...... 209 7.1 Chapter 1 ...... 209 7.2 Hudson Bay ...... 209 7.3 Antarctic Peninsula ...... 213

Bibliography ...... 217

Appendices

A Hudson Bay Ecosystem Model Parameters and Details .. 287 A.1 Model Parameters by Functional Group ...... 287

v Table of Contents

A.2 Fisheries Input ...... 325 A.3 Model Fitting Parameters and Data Sets ...... 333 A.4 Model Parameterization and Output ...... 336

B Marine Mammal Mortality Equations ...... 346

C Hudson Bay Bird Species ...... 348

D Hudson Bay Fish Species ...... 353

E Hudson Bay Model Vulnerabilities ...... 355

F Hudson Bay Mixed Trophic Impacts ...... 358

G Hudson Bay Monte Carlo CV Values ...... 367

H Hudson Bay Monte Carlo Results ...... 369

I Hudson Bay Ecosim Biomass Trends by Species ...... 373

J Antarctic Peninsula Ecosystem Model Parameters and De- tails ...... 376 J.1 Model Parameters by Functional Group ...... 376 J.2 Ecosim Input Parameters ...... 426 J.3 Model Parameterization and Output ...... 437

K Antarctic Peninsula Model Vulnerabilities ...... 453

L Antarctic Peninsula Model Mixed Trophic Impact Values 458

M Antarctic Peninsula Monte Carlo CV Values ...... 466

N Antarctic Peninsula Monte Carlo Results ...... 468

O Antarctic Peninsula Monte Carlo Graphs ...... 471

P Antarctic Peninsula Model Biomass Trends By Species . 473

vi List of Tables

2.1 Harvest trends used in the Hudson Bay Ecosim model . . . . 26 2.2 Balanced Ecopath model parameters ...... 29 2.3 Trophic level of the ecosystem (TLE) and catches (TLC )... 37

3.1 Time series data used for Antarctic Peninsula model ﬁtting . 55 3.2 Balanced Ecopath model parameters for the Antarctic Penin- sula ...... 62 3.3 Antarctic Peninsula trophic level of the ecosystem TLE and catches TLC ...... 70

4.1 Hudson Bay future climate and hunting scenarios ...... 86 4.2 Summary of harvest values and hunting/ﬁshing mortalities used for the initial Hudson Bay Ecopath model and future hunting scenarios ...... 89 4.3 Trophic level of ecosystem (TLE) and catches(TLC ) for each future Hudson Bay simulation ...... 94

5.1 Catches and ﬁshing mortalities for each Antarctic Peninsula future scenarios...... 129 5.2 Antarctic Peninsula future harvest and climate scenario names.130 5.3 Trophic level of ecosystem (TLE) and catches(TLC ) for the Antarctic Peninsula future simulations ...... 133

6.1 Parameter inputs for Hudson Bay economic model equations 188 6.2 Statistics for hunting communities in Hudson Bay ...... 195 6.3 Economic value including cost sharing and opportunity cost . 200 6.4 Contribution of revenue to each community ...... 201

A.1 Hudson Bay Ecopath marine mammal input parameters . . . 289 A.2 Hudson Bay ﬁshing mortality based on per capita consumption rates ...... 306 A.3 Calculated input parameters for Hudson Bay ﬁsh groups . . . 309

vii List of Tables

A.4 Comparison of parameters for benthic functional groups from high latitude Ecopath models...... 320 A.5 Arctic killer whale harvests ...... 326 A.6 Time series data for Hudson Bay Ecosim ﬁtting ...... 333 A.7 Balanced Ecopath model parameters for Hudson Bay . . . . . 338 A.8 Coeﬃcient of variation values used for Monte Carlo estimates 339

B.1 Marine mammal survivorship curve parameters ...... 347

C.1 Hudson Bay bird species ...... 348

D.1 Hudson Bay ﬁsh species by functional group ...... 353

E.1 Hudson Bay model vulnerabilities ...... 355

F.1 Hudson Bay mixed trophic impact results ...... 359

G.1 Hudson Bay Monte Carlo coeﬃcient of variation values . . . . 368

J.1 Southern Ocean cetacean estimates ...... 376 J.2 Antarctic Peninsula marine mammals parameter values . . . 378 J.3 Antarctic Peninsula Ecopath penguin parameters ...... 390 J.4 Antarctic Peninsula Ecopath ﬁsh parameters ...... 399 J.5 Benthic habitat by depth range for the Antarctic Peninsula . 404 J.6 Antarctic Peninsula Ecopath parameters for invertebrate groups411 J.7 Natural mortality rates of Antarctic krill ...... 420 J.8 Multistanza parameters for krill functional groups...... 421 J.9 Antarctic Peninsula time-series data ...... 430 J.10 Antarctic Peninsula balanced Ecopath model parameters . . . 439 J.11 Antarctic Peninsula Monte Carlo estimates ...... 444

K.1 Antarctic Peninsula vulnerabilities used for the ﬁtted model . 454

L.1 Antarctic Peninsula mixed trophic impact results ...... 459

M.1 Antarctic Peninsula Monte Carlo CV values ...... 467

N.1 Antarctic Peninsula Monte Carlo results ...... 469

viii List of Figures

2.1 Hudson Bay map ...... 19 2.2 Mean sea surface temperature and ice cover for Hudson Bay . 24 2.3 Changes in fish abundance as measured by the diets of thick- billed murres ...... 25 2.4 Food web linkages in the Hudson Bay ecosystem model . . . 31 2.5 Biomass trends for functional groups fitted to time-series data 34 2.6 Contribution of fish groups to total fish biomass ...... 35 2.7 Hudson Bay Monte Carlo simulation results ...... 36 2.8 Changes in biomass by Hudson Bay model scenario ...... 39

3.1 Map of Antarctic Peninsula (FAO area 48.1) ...... 51 3.2 Krill and fish catches presented by year ...... 57 3.3 Environmental drivers used in the model fitting process . . . 59 3.4 Antarctic Peninsula fitted model ...... 66 3.5 Antarctic Peninsula Monte Carlo biomass estimates . . . . . 69 3.6 Antarctic Peninsula Ecosim simulation results ...... 71

4.1 Hudson Bay environmental data used in future model simulations ...... 88 4.2 Hudson Bay future changes in biomass by model scenarios . . 92 4.3 Future scenario changes in biomass for sandlance ...... 99 4.4 Future scenario changes in biomass for capelin ...... 100 4.5 Future scenario changes in biomass for gadiformes ...... 101 4.6 Future scenario changes in biomass for northern walrus . . . . 105 4.7 Future scenario changes in biomass for ringed seals ...... 106 4.8 Future scenario changes in biomass for narwhal ...... 107 4.9 Marine mammals and ﬁsh biomass results by future scenario 117 4.10 Invertebrates, zooplankton and producer biomass results by future scenario ...... 118 4.11 Marine mammals and ﬁsh mortality results by future scenario 119 4.12 Invertebrates, zooplankton and producers mortality results by future scenario ...... 120

ix List of Figures

5.1 Antarctic Peninsula sea ice and SST trends ...... 127 5.2 Antarctic Peninsula future biomass changes by species and scenario ...... 134 5.3 Future scenario changes in biomass for copepods ...... 139 5.4 Future scenario changes in biomass for juvenile krill . . . . . 140 5.5 Future scenario changes in biomass for adult krill ...... 141 5.6 Future scenario changes in biomass for salps ...... 142 5.7 Future scenario changes in biomass for myctophids ...... 146 5.8 Future scenario changes in biomass for large deep demersals . 147 5.9 Future scenario changes in biomass for toothfish ...... 148 5.10 Future scenario changes in biomass for Adelie penguins . . . 152 5.11 Future scenario changes in biomass for minke whales . . . . . 155 5.12 Future scenario changes in biomass for Antarctic fur seals . . 156 5.13 Antarctic Peninsula marine mammal ending biomass by scenario ...... 166 5.14 Antarctic Peninsula fish and invertebrate ending biomass by scenario ...... 167 5.15 Antarctic Peninsula invertebrate and plankton ending biomass by scenario ...... 168 5.16 Antarctic Peninsula marine mammal ending mortality by scenario ...... 169 5.17 Antarctic Peninsula fish and invertebrate ending mortality by scenario ...... 170 5.18 Antarctic Peninsula invertebrate and plankton ending mortality by scenario ...... 171

6.1 Map of communities in Nunavut portion of Hudson Bay hunting narwhal or beluga ...... 176 6.2 Distributions and 95% CI for total revenue, total cost, total use value, and total use value including opportunity cost . . . 197 6.3 Average per capita use value for beluga and narwhal hunts with cost charing and opportunity cost ...... 198

A.1 Reported catches of narwhal from 1977-2007 for Hudson Bay communities ...... 327 A.2 Catches of beluga whales form 1970-2007 as aggregated by stock...... 330 A.3 Regression of community population size in Nunavut . . . . . 332 A.4 Polar bear mediation function ...... 334

x List of Figures

A.5 Antarctic Peninsula ending biomass results for producers and detritus ...... 342 A.6 Antarctic Peninsula ending biomass results for zooplankton and benthic groups ...... 343 A.7 Antarctic Peninsula ending biomass results for ﬁsh and seabirds344 A.8 Antarctic Peninsula ending biomass results for marine mammals345

H.1 Hudson Bay marine mammal Monte Carlo biomass and P/B results ...... 370 H.2 Hudson Bay ﬁsh Monte Carlo biomass and P/B results . . . . 371 H.3 Hudson Bay plankton Monte Carlo biomass and P/B results . 372

I.1 Hudson Bay fitted model biomass trends for marine mammal and fish groups ...... 374 I.2 Hudson Bay fitted model biomass trends for benthic and plankton groups ...... 375

J.1 Numbers of penguin breeding pairs at Anvers Island, Antarc- tic Peninsula ...... 388 J.2 Krill fishing effort used in model fitting ...... 427 J.3 Krill catches used in model fitting ...... 428 J.4 Antarctic Peninsula krill abundance and biomass trends . . . 429 J.5 Antarctic Peninsula salp abundance trends ...... 429 J.6 Mediation function used for larval and juvenile krill...... 433 J.7 Mediation function used for salps...... 434 J.8 Antarctic Peninsula Ecosim changes in biomass for producers and detrital groups...... 447 J.9 Antarctic Peninsula Ecosim changes in biomass for zooplankton groups...... 449 J.10 Antarctic Peninsula Ecosim changes in biomass for benthic groups...... 449 J.11 Antarctic Peninsula Ecosim changes in biomass for fish groups.450 J.12 Antarctic Peninsula Ecosim changes in biomass for penguin and flying bird groups...... 451 J.13 Antarctic Peninsula Ecosim changes in biomass for marine mammal groups...... 452

O.1 Antarctic Peninsula Monte Carlo biomass results ...... 472

P.1 Antarctic Peninsula biomass trends for SST and SOI ﬁtted models ...... 474

xi Acknowledgements

There are may people who have contributed time, support, wisdom, and encouragement. Without them this thesis would not exist. First to my advisor Tony Pitcher for allowing me the opportunity to pursue this degree, and to my committee members Evgeny Pakhomov and Andrew Trites for their advice throughout this process. To Villy Christensen for his constant advice and technical assistance throughout my degree. For all of my co- workers who provided technical assistance: Sherman Lai, and Dalai Felinto for assisting with Ecopath errors. Joreen Steenbeek for going above and beyond his job fixing Ecopath bugs, and all of his help with programming. Thanks to Steve Martell and Rob Ahrens for expanding my knowledge of R and always helping to fix code. To Rashid Sumaila, thank you for your endless guidance on all things economic, and for always listening to my ideas. There have been many students past and present who provided academic and personal assistance: Brooke Campbell for providing GIS maps, Megan Bailey for providing food web images for publications, and Laura Tremblay- Boyer for technical help. Thanks to Chiara Piroddi, Divya Varkey, Leigh Gurney, Cam Ainsworth, Colette Wabnitz, and Robyn Forrest for providing peer support on modeling questions. For the Hudson Bay portion of the thesis, numerous researchers provided their time and resources in order to make the ecosystem model and the economics chapters possible. To them I would like to say thanks: Jeff Higdon, Elly Chmelnitsky, Patt Hall, Jack Orr, Blair Dunn, Tara Bortoluzzi, Lisa Loseto, Sebastian Luque, and Bruce Stewart. Travel to Hudson Bay was provided by the Cecil and Kathleen Morrow Scholarship, and provided unparalleled insight to the dynamic of the ecosystem through first-hand experience, thank you. A special thanks to Steve Ferguson for the opportunity

xii Acknowledgements to work with him as part of the IPY project on Hudson Bay, for the opportunity to participate in ﬁeldwork, and endless advice on Hudson Bay. In addition to academic support, many colleagues and friends have provided invaluable support through my time at UBC. For that I would like to thank Megan Bailey, Rachael Louton, Shannon Obradovich, Rhona Goven- der, Brooke Campbell, Andres Cisneros, Roseti Imo, Prammod Ganapathi- raju, Sarika Cullis-Suzuki, Jennifer Jacquet, Meaghan Darcy, Erin McCul- loch, Liz Martell, Chiara Piroddi, and Maria Espinosa.

xiii Dedication

To William, Diane, and Travis.

xiv Chapter 1

Introduction

The main aim of this thesis is to address the impacts of harvest and environmental changes on two polar ecosystems; one Arctic and one Antarctic. Both regions aim to manage with an ecosystem-based approach (CCAMLR, 1980; Anonymous, 2006), implying that exploitation of target species should not cause destruction of other species. For both ecosystems, the following questions formed the chapters presented in the thesis. (1) What did the past ecosystem look like? (2) What factors caused past changes in the ecosystem? (3) How will these factors continue to impact the ecosystem in the future? And lastly, (4) For the Arctic ecosystem where people rely on harvest for subsistence, how might these changes aﬀect these communities? This ﬁrst chapter aims to provide a background into both of the case study areas. First, I provide information on the geographic regions, and the environmental factors that shape them. Second, I address the management of each area, and the goals of the managers within the context of the ecosystem. Last, this chapter provides a summary of the questions and research completed in each chapter of the thesis.

1.1 Ecosystem-Based Management

The focus of this thesis is on two polar ecosystems with the intent to identify prominent stressors that have, or will in the future, alter ecosystem structure. The overall goal is to provide this information so that future research and management decisions can take into account the ecosystem dynamics of environmental change and exploitation. Fisheries regulation within the context of an entire ecosystem has become more prominent in recent years with the development of management strategies such as ’Ecosystem-Based

1 1.1. Ecosystem-Based Management

Management’ (EBM), ’Ecosystem-Based Fisheries Management’ (EBFM), and ’Ecosystem Approach to Fisheries’ (EAF). Despite the different termi- nologies, these management strategies share many of the same goals such as maintaining natural structure and function of the ecosystem, preventing declines of target and non-target species, and identifying environmentally sustainable development of resources (Ward et al., 2002; Hall and Main- prize, 2004; Pitkitch et al., 2004; Scandol et al., 2005). It has been noted that management priorities should be focused on the ecosystem as a whole, rather than just target species (Pitkitch et al., 2004). Yet as these phrases have only in recent years begun appearing in the literature and management plans, the foundations of EBM are deep-rooted within many international agreements. The United Nations Convention on the Law of the Sea, an international agreement between 162 countries, notes harvest of species should be accomplished while maintaining or restoring populations of harvested and dependent species for both coastal and high seas fisheries (United Nations, 1982, articles 61 and 191). The same year the Commission for the Conservation of Antarctic Marine Living Resources came into effect regarding the management of the Antarctic. Article II of the convention specifically addresses harvest in that it should prevent irreversible changes in the ecosystem and maintain ecological relationships between harvested, dependent, and related populations (CCAMLR, 1980; Constable et al., 2000). In 1992, the Rio Declaration on the Environment and Development called for the use of the precautionary principle in order to protect the environment (United Nations, 1992, principle 15). Individual countries such as the US, Canada and Australia have also integrated aspects of EBM into their management plans for specific areas or fisheries (Quinn and Theberge, 2004; Scandol et al., 2005; Pace, 2009). Evaluation of aquatic ecosystems through models such as EwE (Ecopath with Ecosim) and Atlantis allow for fisheries assessments at the ecosystem level (Scandol et al., 2005), and the first four chapters of this thesis address this. In addition to ecological aspects of EBM, social and economic goals are also considered for the success of EBM (Hilborn et al., 2004). Scandol et al. (2005) noted the importance of management to recognize that in addition to

2 1.2. Study Areas harvesting, there are additional uses and values of the ecosystem that must be considered. As human values drive the management process, and the human uses and values are an important component to EBM, these must be considered for management to be successful (Ward et al., 2002). It is for these reasons that I have included a chapter estimating the economic use value of hunts in Hudson Bay.

1.2 Study Areas

Two regions were chosen as case studies for the thesis; one from the Arctic and one from the Antarctic. From the Arctic, Hudson Bay was chosen. Al- though Hudson Bay is considered sub-Arctic in location, its weather patterns are reﬂective of a higher latitude region, with many high Arctic species residing there such as polar bears (Stirling and Parkinson, 2006). In addition, collaboration with researchers at the Department of Fisheries and Oceans Canada (DFO) Cental and Arctic Division in Winnipeg, Manitoba as part of their International Polar Year (IPY) Global Warming and Arctic Marine Mammal (GWAMM) project dictated the study area. For the Antarctic case study, the Antarctic Peninsula was selected. It is considered one of the fastest warming areas in the world (Anisimov et al., 2001; Hansen et al., 2006a), while other areas of the Antarctic have shown to be in a cooling trend (Turner et al., 2005). The central species in this ecosystem, krill (Euphausia superba), have shown declines linked to environmental changes in addition to being directly harvested (Atkinson et al., 2004; CCAMLR, 2008b).

Hudson Bay

Physical Environment

Hudson Bay is a large, shallow, low nutrient marine area which freezes and thaws annually (Markham, 1986; Stewart and Lockhart, 2005; Stewart and Barber, 2010). Ice and temperature within this region are more reﬂective of a high Arctic ecosystem, allowing species normally found at higher latitudes to be found within Hudson Bay (Maxwell, 1986; Stewart and Barber,

3 1.2. Study Areas

2010). The Hudson Bay watershed is the second largest in Canada, capturing roughly 30% of all Canadian runoﬀ (Natural Resources Canada, 1999). The timing of freshwater and nutrients inputs can have large impacts on the type and amount of annual primary production (Stewart and Barber, 2010). Seawater enters and exits via Hudson Strait, and circulates in a counter- clockwise direction (Stewart and Lockhart, 2005). The cooler deeper waters characteristic of Hudson Strait potentially act as a thermal barrier, preventing species from entering Hudson Bay. This divide is believed to be a choke-point for migratory species, such as killer whales, and it is believed that this divide will open as climate warms (Higdon and Ferguson, 2009). Being classiﬁed as a polar ecosystem, ice is an important component in the life cycle of many organisms, ranging from algae frozen within the sea ice (Horner et al., 1992), to top predators such as polar bears who use ice as a foraging platform (Stirling et al., 2004). Increases in temperature combined with lengthening of the ice-free season have increased concern for species residing in Hudson Bay (Parkinson et al., 1999; Gagnon and Gough, 2005; Hansen et al., 2006b).

Resource Uses

Hudson Bay has been used for roughly 4000 years by nomadic hunters who depended on marine mammals, fish and land animals such as caribou for subsistence (Stewart and Lockhart, 2005). European activity in the region started in the 17th century, as explorers searched for the northwest passage. Henry Hudson (Hudson Bay’s namesake) was the first recorded explorer into Hudson and James Bays (Francis and Morantz, 1983). Continued ex- peditions into the region and the abundance of available furs, primarily beaver, led to the establishment of the Hudson’s Bay Company (Stewart and Lockhart, 2005). Harvest of fur-bearing animals by natives increased to meet the supply demands of the Europeans, although this conflicted with ancient spiritual beliefs (Sokolow, 2003). While other animals were also harvested for fur, beavers were specifically targeted, with their pelts used as currency between Europeans and Aboriginals before populations crashed

4 1.2. Study Areas

(Homren, 2004). As the fur trade declined, interest in whaling became more prominent (Francis and Morantz, 1983). Prior to the commercial whaling of bowhead whales, Hudson’s Bay Company had small-scale unsuccessful attempts at commercial whaling operations for belugas (Reeves and Mitchell, 1987). The Northwest Company, a fur trading company formed in Montreal which later merged with the Hudson’s Bay Company, and the Hudson’s Bay Company operated posts in Hudson and James Bays related to whaling and trade (see Stewart and Lockhart, 2005, table 11-3 for a full list of settlements). American and Canadian vessels commercially harvested bowhead whales from 1860 to 1915, causing a large population decline before commercial whaling commenced in the region (Ross, 1974). Presently, subsistence harvest is allowed for many species with varying levels of regulation. Narwhal, beluga and polar bears have quotas and are harvested annually, while bowhead whales are considered endangered and are rarely harvested (DFO, 1998; Cosens and Innes, 2000; Hammill, 2001; Lunn et al., 2002). Seals, walrus, birds, fish and invertebrates are harvested by Aboriginals (Inuit and Cree) without a license and can be taken throughout the year (Berkes, 1977; Wein et al., 1996; Stewart and Lockhart, 2005). A license is required for sport hunters to harvest birds within the area (Stew- art and Lockhart, 2005). The only commercial fishing operation is for Arctic char along the river mouths, but this fishery yields small catches (Carder and Peet, 1983; DFO, 1997). Presently, most communities surrounding Hudson Bay are inhabited by first nations, making up 85% of the total population in Nunavut, most of which are Inuit (Statistics Canada, 2006).

Management

The territory encompassing Hudson Bay is divided between the provinces of Manitoba, Ontario and Quebec, and the Nunavut territory. Within Que- bec, indigenous people (Inuit and Cree) live in Nunavik, the name for the northern third of the province. The ﬁrst major agreement between Quebec and the Inuit was the James Bay and Northern Quebec Agreement in 1978 to give environmental and social protection (Anonymous, 1975).

5 1.2. Study Areas

The territory of Nunavut was established in 1999, separating it from the pre-existing Northwest Territories. Management for the Ontario and Manitoba portions of Hudson Bay is regulated by DFO, while the Nunavut portion is governed by the Nunavut Wildlife Management Board (NWMB). The Nunavut Land Claims Agreement, signed into effect in 1993, gives management authority of wildlife within Nunavut to the NWMB (Nunavut Land Claims Agreement, 1993). The NWMB consists of appointed members which are responsible for establishing, modifying, or removing levels of total allowable harvest. In 2006, the Nunavik Inuit Land Claims Agree- ment established the Nunavik Marine Region Wildlife Board (NMRWB) and granted Nunavik Inuit the right to harvest wildlife species to fulfil their economic, social, and cultural needs (Anonymous, 2006). From 1996-2001 the NWMB conducted the Nunavut Wildlife Harvest Study to collect data for species within Hudson Bay for which it was responsible (Nunavut Wildlife Managament Board, 2000). This harvest study was to help provide baseline information for all of Nunavut, for which to base total allowable harvests, primarily for marine mammal species (Priest and Usher, 2004). The total allowable harvest must be approved by the NWMB, and they retain the right to alter harvest levels in the future. The Nunavik parallel to this board, NMRWB, is responsible for the harvesting of species within the Nunavik and James Bay portions of Hudson Bay regarding Inuit harvest. The Canadian government, specifically DFO, can disallow decisions set by the NWMB for reasons of conservation, public safety, or public health (Nunavut Land Claims Agreement, 1993). The federal government also holds the power to interfere regarding harvest in Nunavik.

Antarctic Peninsula

Physical Environment

The Southern Ocean surrounds Antarctica, and while there are no physical barriers separating this ocean from the surrounding waters, the Antarctic Polar Front (or Antarctic convergence) is where the colder Antarctic waters sink below the warmer sub-Antarctic waters forming a thermal barrier

6 1.2. Study Areas between 50◦S to 60◦S (Knox, 1994). Two main current systems occur in the Antarctic. The first is the Antarctic Circumpolar Current (ACC) or west wind drift, which flows east around the continent, near the Antarctic Convergence and carries with it nutrient rich upper circumpolar deep water (Tynan, 1998; Fallon and Stratford, 2003). The second is the coastal current (east wind drift) which moves towards the west as a counter current to the ACC. It moves close to the continent, and is responsible for forming eddies close to the shelf (Knox, 1994). The Antarctic Peninsula is the only land mass to extend from the continent. Along with the tip of South America, this peninsula impedes both wind and ocean currents in the Southern Ocean through Drake Passage, the area between the two peninsula tips (Fallon and Stratford, 2003). The ACC moves faster through this area and constricts to bring in warmer water originating from the Bellingshausen Sea (to the west) towards the Scotia Sea (to the east) (Hewitt et al., 2002). The constriction of the ACC in this area forces the southern boundary (southern limits of the ACC) close to the continent at the Antarctic Peninsula (Tynan, 1998). In addition to the southern boundary, wind and bathymetry also contribute to the high productivity of the area and the large biomass of Antarctic krill (Euphausia superba) (Prezelin et al., 2000). Seasonal ice conditions are also a feature of the region, with the extent of sea ice as an important factor for many ice-associated species. Observed declines in sea ice and increases in temperature are more extreme at the Antarctic Peninsula than other Antarctic locations (Doake and Vaughan, 1991; Anisimov et al., 2001; Cook et al., 2005; Hansen et al., 2006a). One of the most studied species in the Antarctic, krill, has been identified to be a key link in the Antarctic food web, in addition to having stages of its life history associated with sea ice (Marschall, 1988; Daly, 1990; Moline et al., 2000). Future changes to the environment are expected to impact krill, and subsequently, the rest of the food web.

7 1.2. Study Areas

Resource Uses

Resource use in the Antarctic began with the discovery of seals at South Georgia before moving on to whaling, fishing, and finally krill harvest. Seal- ing in the Antarctic began after Captain Cook reported large populations of fur seals in the sub-Antarctic islands (Kriwoken and Williamson, 1993). Seals were targeted for their pelts, with Antarctic and sub-Antarctic fur seals making up the majority of catches in the late 1700s to early 1800s with over 1.2 million harvested by 1822 (Agnew et al., 2000). While fur seals were the early targets of sealing fleets, elephant, Ross, crabeater, and Weddell seals have all been targeted, with many populations being largely reduced by harvest (Fallon and Stratford, 2003). The Convention on the Conservation of Antarctic Seals (CCAS) was established in the 1970s to set catch limits for seals, and prevents the commercial harvest of seals south of 60◦S (Agnew et al., 2000). During the era of seal harvest, penguins were also harvested, primarily for oil (Agnew et al., 2000). Whaling began as seal resources declined. Commercial whaling in the Antarctic was initiated in 1892 and continued until 1982, when the International Whaling Commission (IWC) issued a moratorium on whaling (Fallon and Stratford, 2003; International Whaling Commission, 2009). Whaling started at South Georgia before expanding to other sub-Antarctic islands and further south to the continent (Agnew et al., 2000). Humpback, minke, blue, sei, southern right and sperm whales have all been harvested in the Southern Ocean (Fallon and Stratford, 2003). Due to large declines in many whale populations, the IWC assigned humpback and blue whales protected status in 1963 and 1964, respectively (Kriwoken and Williamson, 1993). The Southern Ocean was declared a whale sanc- tuary in 1994 by the International Whaling Commission prohibiting ship or land-based whaling operations (Agnew et al., 2000). Japan objects to the moratorium and continues to harvest whales, claiming scientific whaling, with their primary target being minke whales in the Southern ocean (Agnew et al., 2000). A fishery for finfish species; mackerel icefish (Champsocephalus gunnari),

8 1.2. Study Areas spiny icefish (Chaenodraco wilsoni), marbled rockcod (Notothenia rossi), humped rockcod (Notothenia gibberifrons), blackfin icefish (Chaenocephalus aceratus), and ocellated icefish (Chionodraco rastrospinosus) was open from 1978 to 1989 in the Antarctic Peninsula area (Kock, 1998). Since the fishery closure there is currently some exploratory fishing, but no re-opening of finfish fishing. Patagonian and Antarctic toothfish (Dissostichus eleginoides and Dissostichus mawsoni) were harvested within the Southern Ocean starting in the mid 1980s (Agnew et al., 2000). The majority of catches from this fishery are taken from South Georgia, with limited catches recorded for only a few years within the Antarctic Peninsula area (CCAMLR, 2008b). Following a decade of exploratory fishing Antarctic krill, became a target species when the commercial fishery opened in 1972 (Nicol and Endo, 1999; Agnew et al., 2000). Japan, the Soviet Union, and Russia obtain the majority of krill catches, with large numbers harvested from the Antarctic Peninsula (Nicol and Endo, 1999; CCAMLR, 2008b). The fishery operates year-round with catches closer to the continent occurring primarily in the austral summer, and catches from sub-Antarctic areas (South Georgia) in winter months (Nicol and Endo, 1999). Observed declines in krill stocks over the last 20 years are associated with changes in environmental conditions (Atkinson et al., 2004). While the quota for krill is much higher than annual catches, in 2010 catch biomass increased to nearly double the values from 1994-2009 (Nicol et al., 2012).

Management

The Antarctic Treaty, which entered into force in 1961, established freedom of scientiﬁc information in the Antarctic in addition to establishing its use for peaceful purposes (Anonymous, 1959). Prior to this, the International Whaling Commission was responsible for managing species in the Southern Ocean (Fallon and Stratford, 2003). In 1982 the Commission on the Con- servation of Antarctic Marine Living Resources (CCAMLR) was established (CCAMLR, 1980). It has been considered one of the ﬁrst regulating agen- cies to establish an ecosystem approach to managing resources (Constable

9 1.3. Ecopath with Ecosim et al., 2000). In 1985 CCAMLR established the Ecosystem Monitoring Program (CEMP) in order to regulate harvest in accordance with the ecosystem approach. CEMP monitors both harvested and dependent species to estimate predator, prey and environmental performance parameters around the Antarctic (Agnew, 1997). The monitoring program assists CCAMLR in parameteriz- ing models for use in establishing quotas.

1.3 Ecopath with Ecosim

The majority of this thesis applies the Ecopath with Ecosim (EwE) approach to construct ecosystem models and simulate changes over time. The Ewe approach originated with a single mass-balanced Ecopath model based in Hawaii (Polovina, 1984), and has expanded throughout development to include numerous additional features for assessing ecosystems. Temporal simulations (Ecosim) and spatial analysis abilities (Ecospace) were later added to aid in assessments of fishing policies and formation of protected areas (Walters et al., 1997, 1999, 2000). Indices to explore the health of the ecosystem were developed through a series of network analyses (Christensen and Pauly, 1992; Christensen, 1995). Additional features such as automated mass-balance with incorporation of Monte Carlo for better parameter estimation and network analysis have been added throughout development (Kavanagh et al., 2004). EwE is used in over 154 countries, with over 300 papers published, and has been named one of NOAA’s top 10 breakthroughs (NOAA, 2006). An updated version is now in use to allow greater flexibil- ity in user programming and coupling between other modelling programs (Christensen et al., 2007; Buszowski et al., 2009). Ecosystem models, specifically EwE, have been developed to evaluate ecosystem effects of fishing and environmental change (Christensen and Wal- ters, 2004), which are the main objectives of the thesis. Single species models may prove efficient when assessing one species, but they are unable to identify potential impacts caused by linkages within the ecosystem (Fulton and Smith, 2004). Multispecies models are able to identify non-intuitive changes

10 1.4. Thesis Outline in biomass through species interactions within the model, and may assist in evaluating ecosystem impacts of management policies (Walters et al., 1997; Fulton and Smith, 2004). While other modeling tools exist (see Plaganyi, 2007, for a detailed comparison of ecosystem modelling tools), EwE was selected over single species models due to ease of use and scope of the thesis. Ecosystem modelling tools such as Atlantis are considered to be the most complete when assessing entire ecosystems as it represents both biological and physical interactions within an ecosystem, however large amounts of data are required in addition to sub-models to address bio-geochemical interactions (Fulton and Smith, 2004; Plaganyi, 2007), which are beyond the scope of the thesis. Lim- itations to ecosystem and other multi-species approaches to modelling are rooted in the quality and availability of data (Plaganyi and Butterworth, 2004). The EwE approach allows users an existing model framework in addition to the ability of the software to focus on ﬁshery and environmental issues (Christensen and Walters, 2004; Plaganyi and Butterworth, 2004). As models should aim for the ’minimum realistic’ approach to avoid over- parameterization (Fulton et al., 2003), the EwE software was selected as the most capable tool for the thesis.

1.4 Thesis Outline

Managers are becoming increasingly focused on policies that include an ecosystem-based management approach, meaning the context of the ecosystem is considered when policies are focused around a particular species. Ecosystem models can identify potential impacts that a series of single- species models cannot (Fulton and Smith, 2004). Furthermore, management policies focusing on single species have the potential to overlook important indirect trophic linkages to targeted species. Within this thesis, I investigate the impacts of harvest on all species in the ecosystem within the same time scale in conjunction with known or theorized impacts from environmental changes. The goal of this thesis is to identify important stressors to each ecosystem, and how future changes in these stressors may impact ecosystem

11 1.4. Thesis Outline structure.

Chapters 2 and 3

Chapters 2 and 3 use the Ecopath with Ecosim software (Walters et al., 1997; Christensen et al., 2005) to assess past changes in ecosystem structure for Hudson Bay and the Antarctic Peninsula respectively. Models were constructed based on past ecosystem structure and projected forward to the present day focusing on catch and environmental changes that have occurred. Methods for using Ecosim simulations to recreate past catch and environmental trends is well established, and has been explored for a multitude of ecosystems, including the Gulf of Alaska and Aleutian Islands, northern British Columbia, Raja Ampat Indonesia and the northern Ionian Sea (Guenette et al., 2006; Ainsworth et al., 2008b,a; Piroddi et al., 2010). In chapter 2, data from all species are combined to assess the trophic structure of the Hudson Bay food web through diet linkages. As part of the IPY project on marine mammals, this chapter explores the potential causes of changes to marine mammals and the rest of the ecosystem with respect to climate change. Declines in some stocks of marine mammals (polar bears, eastern Hudson Bay beluga and narwhal), have prompted research on the reasons for these changes, in part to determine if climate change has had an impact (Stirling et al., 1999; COSEWIC, 2004a; Stirling et al., 2004; Ham- mill et al., 2009). I first identified the ecosystem structure through literature reviews and assistance from researchers at the Department of Fisheries and Oceans Central and Arctic Division in Winnipeg, Canada. I was able to assess gaps in data, such as the biomass of fish groups, through modeling approaches, such as the Monte Carlo routine in EwE (Christensen and Wal- ters, 2004). After the initial structure of the model was complete, re-creation of past trends in Ecosim were performed. Catch records for marine mammal species were readily available from government records, but information on other species was lacking. Changes in the diets of thick-billed murres indicated shifts in the fish community from benthic to pelagic species (Gaston et al., 2003). This was coupled with information on lower trophic levels

12 1.4. Thesis Outline from other Arctic ecosystems to gain an understanding of past changes to the ecosystem. In chapter 3, the past Antarctic Peninsula ecosystem was recreated in the same manner as the Hudson Bay ecosystem (chapter 2). Previous assessments of the Antarctic Peninsula have utilized the EwE methods (Efran and Pitcher, 2005; Cornejo-Donoso and Antezana, 2008), however, they have not included environmental factors. This chapter expands on past research to incorporate different environmental variables to explain declines in krill biomass, and increases in salp groups (a gelatinous tunicate and perceived competitor of krill) in conjunction with harvesting trends. This chapter tests the likelihood of different environmental variables as causing the changes in salp and krill abundance based on ecological studies (Marschall, 1988; Loeb et al., 1997; Brierley and Watkins, 2000; Atkinson et al., 2004; Lee et al., 2010; Flores et al., 2011). I also explore the effects of increasing the harvest of krill to quota levels. As the krill fishery operates on what is considered a keystone species (Quetin and Ross, 1991; Moline et al., 2004), annual catches are only roughly 10% of the quota limits (Hewitt et al., 2002, 2004). This chapter explores the potential repercussions of harvesting krill at full quota levels.

Chapters 4 and 5

Chapters 4 and 5 build on chapters 2 and 3 respectively, by extending simulations into the future. Ecosim scenarios are routinely used to explore fishing strategies in future scenarios (Araujo et al., 2008; Heymans et al., 2009) particularly in an economic context. However, rather than focusing on maximizing profits or other policy objectives, these chapters explore future ecosystem states and address the ecological structure rather than policy objectives. Each chapter utilizes different levels of harvest and environmental drivers previously identified to assess potential future states of each ecosystem. Data from global climate models GFDL (2010) allowed for environmental drivers to be continued into the future in conjunction with IPCC (Intergovernmental Panel on Climate Change) scenarios. Catch scenarios for

13 1.4. Thesis Outline each ecosystem are based on either current harvest levels or are increased to simulate higher quotas in the future. These chapters identify species within each ecosystem likely to be impacted by harvest or environmental changes in the future.

Chapter 6

Chapter 6 focuses on the human component to the Hudson Bay ecosystem by providing an economic assessment to the harvest of narwhal and beluga. Many species are currently harvested within Hudson Bay by Inuit, however cetacean species have been a prominent focus of the Inuit diet for thousands of years (Stewart and Lockhart, 2004; Freeman, 2005). Narwhal and beluga were selected as the focus for an economic assessment of hunting. Model simulations from chapters 2 and 4 identify declines in narwhal and the eastern Hudson Bay beluga indicating their potential lack of availability in the future. Focussing on these two hunts, the economic use value is explored primarily through the costs and revenues associated with harvesting narwhal and beluga. Past economic assessments in the north have been limited and focused on one or more aspects of individual hunts rather than an overview (Weaver and Walker, 1988; Reeves, 1992a). Previous studies have assessed the economic value of hunting in speciﬁc high Arctic communities Loring (1996), however this has not been attempted for the Hudson Bay region. This chapter provides a summary of economic components associated with the harvesting of these two species. In addition to providing an estimate on the total economic use value for each for theses hunt, costs and revenues are also assessed based on each community participating in the harvest.

Chapter 7

Chapter 7 provides a summary and discusses the results of the thesis in the context of managing ecosystem. Directions for future research and applications to management are presented.

14 Chapter 2

Impacts of Hunting, Fishing, and Climate Change to the Hudson Bay Marine Ecosystem 1970-2009

2.1 Synopsis

An ecosystem model was created for the Hudson Bay region, Canada, for 1970-2010, aiming to identify ecosystem linkages while bringing together research from diverse research sources. The research presented was completed as part of the International Polar Year Global Warming and Arctic Marine Mammal project, focusing on the impacts of climate change on marine mammals. The model presented in detail here synthesizes research spanning all trophic levels for incorporation into the Ecopath with Ecosim (EwE) modeling framework. The Ecopath model, containing 40 functional groups, identifies a previously unestimated fish biomass of 3.42t · km−2 for the region, based on the trophic linkages and diets within the food web. Catch and abundance data for the Hudson Bay region, along with environmental drivers (sea surface temperature and ice cover) were used to re-create past changes to the ecosystem through the fitting of individual groups. The Ecosim model captures many dynamics present in the system, while identifying gaps in existing data for future research and as the basis for work simulating climate change and its impacts on the ecosystem. A general shift in lower trophic levels from a sea ice to benthos to benthic fish pathway to

15 2.2. Introduction one favoring pelagic phytoplankton to zooplankton to pelagic fish. Declines in polar bear, narwhal, and eastern Hudson Bay beluga model groups identifies harvest as the main stressor. Simulations testing the model sensitivity to hunting and environmental pressures indicate the biomasses of higher trophic level organisms (marine mammals) are more responsive to hunting pressures while lower trophic levels (benthos, zooplankton) are more easily influenced by climate drivers.

2.2 Introduction

Polar regions are increasing in temperature faster than temperate areas, with Arctic temperature rising at almost twice the rate of the rest of the world (ACIA, 2004). The fourth International Polar Year (IPY) in 2007-2009 highlighted the need for research to increase our knowledge of the dynamics occurring in Polar areas. While Hudson Bay (HB) (ﬁgure 2.1) is geographically considered sub- Arctic, between 50-70◦N, this system reﬂects high Arctic attributes such as climate, biogeography, and higher trophic level animals. For example, polar bears, are found at their lowest latitudinal range in HB, due to the cold winters and the ice available for foraging (Stirling and Parkinson, 2006). Moreover, many species present in this ecosystem have adapted to the seasonal ice cycle, from whales occupying the region during the ice free seasons, and seals breeding on the ice, to the ability of smaller zooplankton to survive winter months using nutrients frozen within the sea ice (Poltermann, 2001; Stewart and Lockhart, 2005). Research in HB has been limited in the past, compared to other Arctic ecosystems. Two surveys of phytoplankton and zooplankton have been completed in HB assessing lower trophic levels; one in 1993 sampling from James Bay (JB) along the east coast of HB into Hudson Strait (HS) (Harvey et al., 1997, 2001), and a second in 2003 running east to west through the middle of HB (Harvey et al., 2006). The most comprehensive benthic summary from numerous locations in HB from 1953 to 1956 (Atkinsor and Wacasey,

16 2.2. Introduction

1989) recorded only the presence of benthic species. Fish are poorly understood, although there is the general belief that fish are not abundant in HB, a situation somewhat verified by unsuccessful commercial fishery ventures in the past (Stewart and Lockhart, 2005). Marine mammals are some of the most well studied species in the region, although only a handful of surveys have been completed for each species (Ferguson et al., 2010). Surface temperatures in HB have increased by 0.5-1.5◦C during 1955- 2005 (Hansen et al., 2006a), and sea ice extent decreased by 2000±900 km−2y−1 between 1978 and 1996 (Parkinson et al., 1999). These changes combined with a longer ice free season (Gough et al., 2004; Gagnon and Gough, 2005) are likely yielding large scale changes to the sympagic marine ecosystem. Ice algae, which contributes up to 57% of primary production in some Arctic regions (Gosselin et al., 1997), and roughly 25% of total production in some areas of Hudson Bay (Legendre et al., 1996), can be stored through the winter within the sea ice. Therefore, the loss of sea ice will alter the availability of algae stored within the sea ice, which will cause shifts in the ecosystem by altering energy transfer to higher trophic levels. Such shifts have already been observed in bird diets as indicated by declines in Arctic cod (Boreogadus saida) and benthic fish species such as sculpins (Family: Cottidae) and zoarcids (Family: Zoarcidae) with increases in pelagic fish such as capelin (Mallotus villosus) and sandlance (Ammodytes spp.). Polar bear populations are at their southern limit in HB, and already experience longer summers than their northern counterparts. Lengthening of the ice free summer is believed to increase nutritional stress as there is less ice to forage on, decreasing their hunting platform, and making polar bears vulnerable to sea ice declines (Stirling and Derocher, 1993; Stirling et al., 1999). Along with environmental changes, human uses of the ecosystem also have the potential to alter the abundance of species. Currently all marine mammal species are hunted annually, with the exception of bowhead where harvest only occurs in specific years. Quotas are imposed on the harvest of certain cetacean species. Seabirds and fish are also harvested, however these are generally unregulated. Since the 1970, human populations

17 2.3. Methods have nearly tripled (Bell, 2002; Statistics Canada, 2006; Nunavut Bureau of Statistics, 2008; Sutherland et al., 2010) with increases in harvest levels for many species also being recorded. Understanding whether these stocks can withstand the continuous pressure of harvest is important, and even more so in conjunction with the impacts of climate change. In order to test the importance of multiple stressors on the ecosystem, we have constructed an ecosystem model to re-create the dynamics from 1970-2009. The ecosystem model was created using the Ecopath with Ecosim software (Buszowski et al., 2009; Christensen et al., 2007), to assess the Hudson Bay ecosystem with a mass-balance model. Through the construction of an Ecopath model, gaps in existing ecosystem knowledge can be identified. For example, biomass of fish populations are obtained by assessing the demands of predators and the amount of fish which can be supported by lower trophic levels, based on food web structure. Ecosim temporal simulations (Walters et al., 1997; Christensen and Walters, 2004) are used to re-create observed changes since 1970, helping to identify causes to changes in ecosystem structure. The model aims to focus on the impact of climate change and hunting on marine mammal species as part of the Global Warming and Arctic Marine Mammal International Polar Year research project, therefore giving marine mammals a greater presence in the model structure. While high and low trophic level organisms are relatively well studied in this region, serious gaps regarding mid trophic level organisms (primarily benthos and fish) exist. Despite these gaps, there is an urgency to understand a system that is subjected to multiple stressors. This modeling approach allows us to infer changes likely occurring to mid-trophic level organisms through existing knowledge of predators and producers.

2.3 Methods

Study Area

The Hudson Bay region often includes Hudson Bay (HB), James Bay (JB), Foxe Basin (FB) and Hudson Strait (HS) (ﬁgure 2.1). This system is one of

18 2.3. Methods the largest bodies of water in the world to freeze over every winter and open up every summer. HB and JB are both categorized by shallow, less productive waters, with large inputs of freshwater from rivers in the spring. Con- versely, Foxe Basin and Hudson Strait have more mixing with the Labrador Sea (Straneo and Saucier, 2008), and are thought to be an important sea ice choke-point for HB, ultimately determining which marine species have access to the region (Higdon and Ferguson, 2009).

100°0'0"W 80°0'0"W 60°0'0"W 4-3 NU Repulse Bay FB

Baker Lake Cape Dorset Coral Harbour Rankin Inlet 60°0'0"N Chesterfield Inlet HS Kimmirut Whale Cove Ivujivik Kangiqsujuaq 60°0'0"N Arviat Salluit Quaqtaq Killiniq Akulivik Kangirsuk Churchill HB Puvirnituq Aupaluk Kangiqsualujjuaq Tasiujaq Inukjuaq Kuujjuaq

Sanikiluaq QB MB Fort Severn Umiujaq

Peawanuck Kuujjuarapik ! Fort Albany JB Chisasibi 50°0'0"N Attaapiskat

ON Moosonee Eastmain 50°0'0"N Waskaganish

80°0'0"W

Figure 2.1: Greater Hudson Bay region including Hudson Bay (HB), James Bay (JB), Hudson Strait (HS), and Foxe Basin (FB). Communities in Nunavut (NU), Manitoba (MB), Ontario (ON), and Quebec (QB) are shown.

Selection of the model area was based on use patterns of marine mammals as their data are more prevalent compared to fish and plankton species. JB was included in the model area due to its similarity to southern HB and the use of this area by certain stocks of polar bears, beluga, seals, and birds. HS and FB were excluded from the model area, as these deeper more productive waters are strongly influenced by currents (Straneo and Saucier, 2008), and are likely to host a different suite of species. For the remainder of

19 2.3. Methods this paper, referral to HB will include JB, an area covering roughly 900,000 km−2 (Legendre et al., 1996). The Ecopath base year model describes the conditions in 1970, with the Ecosim model running from 1970-2009. The base year was chosen as there are no comprehensive estimates of marine species prior to 1970. In addition, changes in environmental conditions and harvest pressure have been documented for this period, thus making for an interesting time to examine the ecosystem dynamics.

Model Equations

Using the Ecopath with Ecosim (EwE) software version 6 (Christensen et al., 2007; Buszowski et al., 2009), an Ecopath or mass-balance model was constructed for 1970. This mass-balance approach links all species or functional groups (groupings of similar species) through diets. Under this assumption there must be enough energy produced by each prey group to account for consumption, migration, ﬁshing mortality, and other mortalities. More speciﬁcally this can be expressed as: ∑ Pi = Bj · M2ij + Yi + Ei + BAi + Pi · (1 − EEi) (2.1) j where Pi is the production of functional prey group i, Bj is the biomass of predator group j with predation mortality on group i of M2ij. Yi is the

fishery catch, Ei is the net migration rate (emigration-immigration), BAi is the biomass accumulation, and EEi is the ecotrophic efficiency (proportion of production that is consumed within the system by predators or exported out of the system due to fishing or migration) for prey i. Equation 2.1 can be re-written as equation 2.2: ∑ Bi · (P/Bi) = Bj · (Q/B)j · DCji + Yi + Ei + BAi + Bi · (P/B)i · (1 − EEi) j (2.2)

Where Bi and Bj are the biomasses of prey (i) and predator (j), (P/B)i is the production to biomass ratio, generally equal to total mortality (Z)

(Allen, 1971), (Q/B)j is the consumption by predator i per unit biomass,

20 2.3. Methods

and DCji is the proportion of prey i in the diet of predator j. Ecopath models are balanced using an algorithm to solve a set of linear equations in the form of Equation 2.2 for each functional group. For each functional group 3 of the 4 basic parameters are imputed (B, P/B, Q/B, EE) along with ﬁshery landings and diet composition, allowing the algorithm to solve for the 4th parameter. Temporal simulations were generated for the time period of 1970-2009 in Ecosim using equation 2.3; ∑ ∑ dBi/dt = gi Qji − Qij + Ii − (MOi + Fi + ei)Bi (2.3) j j

Where dBi/dt represents the change in biomass (B) for group i over the time interval t, with starting biomass Bi. gi∑represents the net growth eﬃ- ciency (production/consumption ratio), the Qji is the total consumption ∑ j on group i, and Qij is the predation of all predators on group i. MOi j represents the other mortality term (for mortality associated with old age),

Fi is the ﬁshing mortality rate, Ii is the immigration rate, ei is the emigration rate, with the combined term Bi · (ei − Ii) as the net migration rate.

The consumption rate of a group, Qij is based on the foraging arena theory where the biomass Bi is further divided into vulnerable and invulnerable proportions to group i’s predators (Walters et al., 1997), and the transfer rate between these two states. Ecosim is based on the foraging arena theory that describes the interactions between predators and prey attributing a vulnerability term. Low values of vulnerability (close to 1) mean that prey production determines the predation mortality (bottom-up interaction) while high values of vulnerability (e.g., 100) mean that predator biomass determines how much prey is consumed (top-down interaction)(Christensen and Walters, 2004).

21 2.3. Methods

Model Inputs and Functional Groups

Ecopath model parameters were set to 1970 values for the marine environment only, estuary and freshwater areas were excluded from the model. A total of 40 functional groups were created; 15 marine mammal groups, 1 bird group including all birds, 9 fish groups, 7 plankton groups, 4 benthic groups, 2 producers, and 2 detritus groups (species for each functional groups are listed in appendix A with full details on input parameters). Marine mammal groups were created to represent individual species, or separate stocks within species if applicable, as changes in stocks have been identified. As there was little knowledge of fish species in the region, fish species were grouped into functional groups based on life history, feeding preferences, and taxonomic characteristics. Plankton and benthic groups were split into those important to higher predators or groups with more information available. Primary producers were split into two groups: ice associated algae and pelagic phytoplankton, with the aim to capture the dynamics of organisms, which are dependant on either one. Ice algae is an important component of the ecosystem, as plankton cells are frozen within the ice each fall and released back into the water column during the spring melt. Contribution of ice algae has been estimated at 25% of total production in parts of HB (Legendre et al., 1996) and can range from 57% in the central Arctic to 3% in surrounding sub-Arctic areas (Gosselin et al., 1997). While some species of phytoplankton and zooplankton have adapted to survive this freeze and return to the water column the following year (Horner et al., 1992), those that do not survive sink through the water column to the benthos. Within the model, exports from the ice algae group are directed to the ice detritus group, which is a major contributor to the diets of benthos. During the spring melt, algal cells are flushed out of the brine channels into the pelagic environment, with a minimum export of ice algae to the benthic community estimated at 20% in southeastern parts of HB (Tremblay et al., 1989). Moreover, accumulation of algal biomass within the sea ice is thought to favor an effective transfer to the benthos, as aggregated algal cells sink up to three times faster than individual algal cells, and damaged cells sink

22 2.3. Methods faster than healthy ones (Tremblay et al., 1989; Riebesell et al., 1991). It has been noted in other Arctic ecosystems that zooplankton biomass is too low during the spring melt to efficiently graze the sinking ice algae, allowing it to sink to the benthos (Legendre et al., 1992). The pelagic production functional group represents all producers not associated with the sea ice. This group exports to a pelagic detritus groups, which is named as it represents the detritus captured by the pelagic producers, rather than its location in the water column. Pelagic production blooms generally occur after the sea ice has started to melt, and remains in the water column longer than ice algae cells (Tremblay et al., 1989). This pelagic bloom sustains pelagic fish and zooplankton into the summer months. In order to simulate changes to primary producer functional groups, data was extracted from the global Hadley Centre Sea Ice and Sea Surface Temperature model (HadISST) from the British Atmospheric Data Centre (2010) and used to force the primary production groups. Warmer temperatures have been shown to alter the mean ice freeze-up and break-up dates by 0.8-1.6 weeks in spring and fall (Hochheim et al., 2010). The availability of ice algae within the model is contingent upon the presence of sea ice; therefore the ice algae group was driven through a forcing function (FF) in the model. The sea ice FF was applied to the ice algae group, as a multiplier of the production rate using the average % cover of sea ice of all cells in the model area. The pelagic phytoplankton functional group was also driven in the model using SST (sea surface temperature), from the same HadISST model. Figure 2.2 shows the average SST and % ice cover by month for 1970-2009 with 95% CI. See appendix A for details on model fitting and selection of drivers. There are no estimates of fish biomass or community composition for HB. Changes in fish populations have been inferred from the diets of thick- billed murres, as biomass is estimated using equations 2.1 and 2.2 in order to satisfy the needs of the predators within the food web, using each group’s respective production ability. There has been a shift from Arctic to sub- Arctic fish composition (figure 2.3); from Arctic and polar cod, sculpins, and zoarcids to capelin and sandlance (Gaston et al., 2003). Although the

23 2.3. Methods

100 5

4 80

3 60 Ice 2 Temp 40

Ice Cover (%) Cover Ice 1

20 0

Sea Surface Temperature ( Temperature Surface Sea ˚C)

0 -1 J FMAMJ J A S OND Month

Figure 2.2: 30 year means and 95% CI for sea surface temperature (SST) and % ice cover calculated by the HadISST global model. diets were collected from the northern limits of HB, due to the gross lack of data on fish populations, diets of birds were the only indication of changes in fish community structure. For all functional groups biomass parameters were expressed in t · km−2, and for non-fish groups were based on surveys collected within the region. For many marine mammal species the total number of animals has been reported. Here, the biomass was extrapolated to the entire region area, which for HB and JB has been estimated at nearly 900,000 km−2 (Legendre et al., 1996). P/B (production to biomass) and Q/B (consumption to biomass) were calculated as a yearly value (y−1) from species specific empirical values if available, with P/B ratios adjusted to account for hunting and fishing mortality in the Ecopath model. Expert opinion, and values from similar ecosystems were used in absence of region specific data. EE (ecotrophic efficiency) was generally estimated by the model, considering the model balanced when the EE value was between 0 and 1 (Christensen et al., 2005). For full descriptions of data incorporated into the model see appendix A. Model fitting included hunting/fishing for species, which are known to be

24 2.3. Methods

100 90 80 70 Arctic Cod 60 Sculpins/ 50 Zoarcids 40 Capelin 30 Sandlance Contribution Contribution to Diet (%) 20 10 0 1984 1987 1990 1993 1996 1999 2002 Year

Figure 2.3: Changes in ﬁsh abundance as measured by the diets of thick- billed murres. Graph recreated from data presented in Gaston et al. (2003). harvested table 2.1.

Model Analysis and Simulations

Monte Carlo simulations were run on the ﬁtted model to estimate plausible ranges of biomass using equation 2.4:

Lxi =x ¯i ± 2 · CV · x¯i (2.4) where Lx represent the limits (upper and lower) of the biomass of group i. The mean biomass,x ¯i, is taken as the value imputed Ecopath starting value. CV values were determined using a pedigree ranking, whereby input parameters are assigned a coeﬃcient of variation (CV) based on the quality of input data, using the pedigree routine in EwE version 5 (Christensen et al., 2005) (see table 2.2 for CV values used in the Monte Carlo Routine). One thousand Monte Carlo simulations were run to ﬁnd ranges of input parameters that allowed the Ecopath model to be balanced. The trophic level (TL) of each species group was calculated for the ini-

25 2.3. Methods

Table 2.1: Hunting and ﬁshing trends as drivers for the Ecosim model (‡indicates information also contributed by Ferguson (pers. comm.))

Fishery Functional Groups Model References Drivers SH Polar Bear Southern Hudson Bay Polar Landings (Lee and Taylor, 1994; Aars Bear et al., 2005) WH Polar Bear Western Hudson Bay Polar Landings (Lee and Taylor, 1994; Aars Bear et al., 2005) FB Polar Bear Foxe Basin Polar Bear Landings (Lee and Taylor, 1994; Aars et al., 2005) Killer whale Killer Whale Landings (Higdon, 2007)‡ Bowhead Bowhead Landings (Higdon, 2008)‡ Narwhal Narwhal Landings (DFO, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998; Stewart and Lockhart, 2005; JCNB/NAMMCO, 2009) N Walrus Northern Hudson Bay Wal- Landings (Strong, 1989; NAMMCO, rus 2005b; Stewart and Lockhart, 2005) S Walrus Southern Hudson Bay Wal- Landings (Strong, 1989; NAMMCO, rus 2005b; Stewart and Lockhart, 2005) Beluga E Eastern Hudson Bay Beluga Landings (JCNB/NAMMCO, 2009; de March and Postma, 2003) Beluga W Western Hudson Bay Beluga Landings (JCNB/NAMMCO, 2009; de March and Postma, 2003) Beluga S James Bay Beluga Landings (JCNB/NAMMCO, 2009; de March and Postma, 2003) Sealing Bearded Seal, Harbour seal, Effort (Stewart and Lockhart, 2005) Ringed Seal, Harp Seal Bird Hunting Birds (all) Effort (Stewart and Lockhart, 2005) Fishing Arctic Char, Atlantic Effort (Stewart and Lockhart, 2005; Salmon, Gadiformes, Booth and Watts, 2007) Sculpins/Zoarcids, Capelin, Sandlance, Other Marine Fish, Brackish Fish tial Ecopath model and each year of the Ecosim simulation using equation 2.3, where primary producers are assigned a TL of 1, and consumers with diets comprised of 100% primary production have a TL of 2 (Christensen et al., 2007). Consumer TL, TLi, is dependent upon the TL of prey items

(TLa,TLb,TLc, for prey items a, b, c, etc.) and the percentage (X) each prey item contributes to the predator’s diet (Xa,Xb,Xc). ∑ TLi = 1 + (Xa ∗ TLa) + (Xb ∗ TLb) + (Xc ∗ TLc)..... (2.5)

26 2.4. Results

Once the TL of each species group is calculated, the mean trophic level of the ecosystem (TLE equation 2.6) and the mean trophic level of the catches

(TLC equation 2.7) can be calculated for each year of the simulations (1970- 2009); ∑ Bi TLE = ∗ TLi (2.6) BE ∑ Ci TLC = ∗ TLi (2.7) CE where Bi and Ci are the biomass and catch for group i, and BE and CE are the biomass and catch of the entire ecosystem, with values represented in t · km−2. Using the model ﬁtted to reported trends in hunting and environmental conditions, further Ecosim simulations were run to test the sensitivity of the ecosystem to hunting and environmental conditions (SST and % ice cover). Two additional simulations were run. First, a ”No Hunting” scenario was run, removing all hunting and ﬁshing mortality from the model while still using environmental drivers (SST and ice cover). Second, a ”Constant Cli- mate” scenario assumed past hunting levels, but the environmental data from 1970 was repeated annually until 2009, to simulate a constant climate condition thus eliminating the declines in sea ice and increases in temperature. This allowed assessment of climate changes in functional groups if driven by environmental changes, hunting pressure, or both.

2.4 Results

Ecopath Output

Using input parameters listed in appendix A, the model was able to estimate the missing parameters in table 2.2. Through the balancing of the model many parameters were reﬁned. Once Ecopath parameters (B, P/B, Q/B, EE) were calculated, P/B ratios were adjusted to account for hunting mortality. The equation used to calculate the P/B ratio for ﬁsh often un-

27 2.4. Results derestimates higher latitude species (Pauly, 1980), and the smaller P/B was causing the mass model to estimate large biomasses of fish. Consequently, these ratios were increased to the upper limits based on the species found within the functional group. Many of the zooplankton groups lacked region specific data for P/B and Q/B, therefore a P/Q ratio of 0.25 was assumed (Christensen et al., 2005), allowing the model to estimate an additional parameter. The EE of birds indicated higher mortality than allowed in the model, therefore the P/B ratio was increased to allow for hunting and predation mortality within the model. Food web structure is displayed in figure 2.4.

28 Table 2.2: Balanced Ecopath model parameters. Biomass (B) and catches are presented in t · km−2, PB (Pro- duction/Biomass ratio), QB (Consumption/Biomass ratio), and BA (Biomass Accumulation) are presented in y−1. EE (Ecotrophic Eﬃciency) and P/Q (Production/Consumption) ratios are dimensionless. Bolded values are estimated by the Ecopath model. The CV (Coeﬃcient of Variation) values for each group are used in equation 2.4 to calculate biomass ranges.

Group Name TL B PB QB EE PQ BA Catches CV

WHB Polar Bear 4.857 0.0005 0.129 2.08 0.414 0.062 - 1.50E-05 0.15 SH Polar Bear 4.906 0.0004 0.154 2.08 0.506 0.074 - 2.20E-05 0.15 Polar Bear Foxe 4.927 0.0002 0.121 2.08 0.304 0.058 - 5.00E-06 0.15 Killer Whale 4.872 2.5E-05 0.151 4.998 0.265 0.03 - 1.00E-06 0.15 Narwhal 4.062 0.0019 0.084 26.182 0.271 0.003 - 3.40E-05 0.15 Bowhead 3.335 0.0109 0.021 5.475 0.384 0.004 0.007 9.00E-06 0.4 Walrus N 3.332 0.0027 0.172 47.123 0.188 0.004 - 8.00E-05 0.25 Walrus S 3.452 0.001 0.097 33.778 0.143 0.003 - 6.00E-06 0.25 Bearded Seal 3.866 0.0037 0.176 14.262 0.791 0.012 - 0.000167 0.25 Harbour Seal 3.971 0.001 0.125 18.612 0.074 0.007 - 2.00E-06 0.25 Ringed Seal 4.077 0.0469 0.158 17.272 0.413 0.009 - 0.000393 0.25 Harp seal 4.103 0.001 0.126 15.66 0.688 0.008 - 1.40E-05 0.25 Beluga E 3.694 0.0021 0.066 21.448 0.22 0.003 -0.004 3.30E-05 0.15 Beluga W 3.873 0.0247 0.064 16.713 0.133 0.004 0.01 6.05E-05 0.15 Beluga James 3.869 0.0015 0.087 16.623 0.679 0.005 - 1.40E-05 0.15 Seabirds 3.839 0.065 0.37 17.258 0.95 0.021 - 0.000325 0.4 Arctic Char 3.3 0.412 0.2 1.5 0.95 0.133 - 4.62E-07 0.1 Atlantic Salmon 3.45 0.148 0.52 7.15 0.95 0.073 - 1.32E-08 0.1

Continued on Next Page 29 Table 2.2 Continued

Group Name TL B PB QB EE PQ BA Catches CV

Gadiformes 3.235 0.853 0.47 1.85 0.95 0.254 - 2.64E-07 0.1 Sculpins/ Zoarcids 3.188 0.382 0.7 3.269 0.95 0.214 2.64E-07 0.1 Capelin 3.132 0.488 1.7 4.8 0.95 0.354 - 1.32E-07 0.1 Sandlance 3.128 0.705 0.85 3.45 0.95 0.246 - 3.96E-08 0.1 Sharks/Rays 4.033 3.18E-06 0.22 1.25 0.95 0.176 - - 0.1 Other Marine Fish 2.948 0.374 0.932 3.018 0.95 0.309 - 6.60E-08 0.1 Brackish Fish 3.216 0.055 3.5 5.798 0.95 0.604 - 2.64E-08 0.1 Cephalopods 3.645 0.227 1.5 5 0.95 0.3 - - 0.25 Macro-Zooplankton 2.711 7.5 1 3 0.278 0.333 - - 0.25 Euphausiids 2.787 2.148 3.3 13.2 0.8 0.25 - - 0.15 Copepods 2.05 4.015 16 64 0.472 0.25 - - 0.15 Crustaceans 2.41 1.8 3.6 14.4 0.584 0.25 - - 0.15 Other Meso-Zooplankton 2.336 1.21 10 40 0.556 0.25 - - 0.15 Micro-Zooplankton 2 2.235 15 45 0.95 0.333 - - 0.25 Marine Worms 2.275 5.93 0.6 4 0.95 0.15 - - 0.1 Echinoderms 2.575 8.708 0.3 1 0.95 0.3 - - 0.1 Bivalves 2.148 5.942 0.57 6.3 0.95 0.091 - - 0.1 Other Benthos 2.091 3.139 2.5 12.5 0.95 0.2 - - 0.1 Pelagic Production 1 8 46.865 - 0.8 - - - 0.15 Ice Algae 1 3.5 46.197 - 0.65 - - - 0.15 Ice Detritus 1 0.009 - - 0.904 - - - - Detritus 1 0.33 - - 0.224 - - - - 30 2.4. Results

5 PolaPolarr BeBearar Killer Whale

4 Ringed Seal Narwhal Beluga Bearded Seal Harbour/Harp Seall Arctic Char Seabirds Bowhead Sandlance Walrus Capelin 3 Gadiformes Sculpins/Zoarcids Marine/Brackish Euphausiids Fish Zooplankton

Echinoderms Crustaceans 2 Worms OtOtherhe BeBenthosnt ho BiBivalveslv Copepods

1 Ice Detritus Pelagic Detritus Ice Algae Pelagic Production

Figure 2.4: Food web linkages in the HB ecosystem with respect to Trophic Level (horizontal lines). Linkages between functional groups were drawn for prey contributing 10% or more to the diet of a predator. For functional groups with more than one species, graphical representation of one species within the group was used. Certain functional groups were combined to be represented by one image; polar bear (western HB, southern HB, and FB polar bear), beluga (eastern HB, western HB, and JB beluga), walrus (northern and southern walrus), harbour/harp (harbour and harp seals), marine/ brackish fish (Atlantic Salmon, sharks/rays, other marine fish, brackish fish), zooplankton (macro-zooplankton, cephalopods, other meso-zooplankton, and micro-zooplankton). Size of image does not indicate biomass size or individual size. All images ⃝c Megan Bailey, 2010 adapted by permission.

31 2.4. Results

Ecosim Fitting

Results of time series fitting, using the data trends provided in table 2.1, and adjusting the vulnerabilities to obtain the observed trends are presented in figure 2.5. See appendix A for full details of vulnerabilities, details of fitting each group, and the general model fitting process. Primary producer groups ice algae and pelagic production were driven with past sea ice and temperature data. Generally, trends for marine mammal functional groups were more easily fit to data, as these time series were created using aerial survey data, and demonstrated gradual changes over time. Data for fitting fish groups provided insight as to general trends of abundance, however the model was unable to simulate the extreme increase in capelin and sandlance populations indicated by their increase in thick-billed murre stomach content, as well as the full decreases in gadiformes and sculpins/zoarcids as suggested by Gaston et al. (2003). This is caused by the high variability of fish time series as they were compiled from the diets of birds, which demonstrated high annual variability. James Bay beluga abundance was not able to increase to levels as high as survey estimates implied. While migration from the EHB beluga group (de March and Postma, 2003; COSEWIC, 2004b) was included in the model (through biomass accumulation) and improved the fit for both EHB and James Bay belugas, the model could not capture the full magnitude of the increase. Conversely, a small decline in EHB belugas was created through hunting mortality and vulnerability settings, but was not fully captured until a negative biomass accumulation component was added to the base Ecopath model, accounting for a loss of this population to the James Bay belugas. Bowhead whales were also unable to increase as rapidly within the model, starting at such a low biomass, and a low P/B, thus a biomass accumulation was added to capture this increase, based on known increases to the population as it recovers from whaling (Higdon and Ferguson, 2010). Rates of biomass accumulation are presented in table 2.2, as annual values (yr−1).

32 2.4. Results

Model Results

Although the fitted model cannot fully capture the changes in fish biomass, most notably, increases in capelin and sandlance shifts in fish composition were reflected. Figure 2.6 identifies the changes in fish structure as measured by their percent contribution to the total fish biomass. Since 1970, the model identifies declines in Gadiformes and benthic species (sculpins/zoarcids) along with increases in pelagic-based species (capelin and sandlance), as noted in Gaston et al. (2003). Within the model these changes are driven by the decline in sea ice, and subsequent declines in ice algae and benthos, food sources for benthic feeding fish. However the pelagic based fish (capelin and sandlance) fare much better, as pelagic production increases along with SST. This promotes the pelagic production- pelagic detritus- zooplankton- pelagic fish chain allowing increases in capelin and sandlance. Monte Carlo simulations (figure 2.7) indicate that the Ecopath model (for the year 1970) can not support higher marine mammal biomasses than the inputted value for most species groups. Ringed seals have the largest starting biomass of any marine mammal group, and also the highest upper limit or largest biomass, which could be supported by the system, followed by WHB beluga and bowhead whales. Ringed seal biomass had a large uncertainty, as population sizes are not well known. However, the model is able to support a large biomass of these seals. Within the model framework, bowheads have the potential to double their biomass while remaining supported by the ecosystem.

33 2.4. Results

Bowhead FB Polar Bear WHB Polar Bear B (t/km2) 0.011 0.014 0.00012 0.00018 1970 1990 2010 1970 1990 2010 0.000201970 0.00040 1990 2010

SHB Polar Bear East HB Beluga James Bay Beluga B (t/km2) 0.002 0.004 0.0010 0.0016

0.000101970 0.00030 1990 2010 1970 1990 2010 1970 1990 2010

West HB Beluga Arctic Cod Sculpins/Zoarcids B (t/km2) 0.026 0.032 0.2 0.6 1.0 0.1 0.3 0.5

1970 1990 2010 1970 1990 2010 1970 1990 2010

Capelin Sandlance B (t/km2) 0 2 4 6 0.2 0.8 1.4 1970 1990 2010 1970 1990 2010

Year Year

Figure 2.5: Biomass trends for functional groups ﬁtted to time-series data. Solid lines represent model values, while open circles represent observed data points. Data points for each group were taken from: bowhead (Higdon, 2008; Higdon and Ferguson, 2010), polar bears (Lunn et al., 2002; Stirling and Parkinson, 2006), beluga (Hammill, 2001; DFO, 2002a; Gosselin et al., 2002; COSEWIC, 2004a; Gosselin, 2005; NAMMCO, 2005a; Hammill et al., 2009), ﬁsh groups (Gaston et al., 2003).

34 2.4. Results

0.4

0.3

0.2

0.1 1970

% of Total Total of %Fish Biomass 0 2009

Funconal Group

Figure 2.6: Percent contribution of each ﬁsh group to total ﬁsh biomass using the Ecopath starting biomass (t · km−2), and the Ecosim generated biomass for the 2009 value.

35 36 iue27 ot al iuainrslsfrEoahsatn ims acltduigE ..Satn biomass Starting 2.4. Eq 2.2. using table calculated biomass in starting presented Ecopath values for CV results and simulation Carlo Monte 2.7: Figure

Biomass (t km− 2 ) 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Ringed Seal Belgua W Bowhead Bearded Seal Walrus N Narwhal Belgua E Walrus S Harbour Seal Harp seal Beluga James

Polar Bear WHB A Polar Bear SH Polar Bear Foxe Killer Whale 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1

Gadiformes Sandlance Capelin Arctic Char Sculpins/ Zoarcids Other Marine Fish Atlantic Salmon

Seabirds B Brackish Fish Sharks/Rays 10 11 12 0 1 2 3 4 5 6 7 8 9

Echinoderms Primary Production MacroZoopl. Bivalves Marine Worms Copepods Ice Algae Other Benthos MicroZoopl. Euphausids Crustaceans Other MesoZoopl. C Cephalopods 2.4. Results

Compared to the inputted biomasses, the ecosystem is able to support higher ﬁsh biomasses than the starting value of 3.42t·km−2 for all ﬁsh groups within HB. The total zooplankton biomass of 18.91t · km−2 falls within the ranges of observed samples, as Harvey et al. (2006) estimated macro and meso-zooplankton from 10-20t · km−2 for central HB, while a few samples from Harvey et al. (2001) reached close to 50t · km−2 northern HB.

Table 2.3: Trophic level of the ecosystem (TLE) and catches (TLC ), presented in 10-year increments. Values were calculated annually from 1970- 2009 using Equations 2.6 and 2.7.

Year TLE TLC 1970 2.457 3.916 1980 2.512 4.033 1990 2.509 4.037 2000 2.541 4.066 2009 2.512 4.032

Trends for trophic levels (TL) of the ecosystem and catches remain relatively stable from 1970-2009 (table 2.3). While catches have a higher trophic level hovering around trophic level 4 (range 3.91-4.07), the ecosystem itself has a much lower trophic level of nearly 2.5 (range 2.45-2.54). This is due to the large proportion of marine mammals being hunted in the system compared to small amount of fish at lower trophic levels. The ecosystem TL remains fairly constant even as declines of polar bears, narwhal, and eastern HB beluga are occurring, as increases in killer whales, seals, and western/JB belugas help to keep the ecosystem TL from declining. While effort for fish, seals, and birds increases based on increases in human populations, these contributions to the overall landings and TL of catches are small in relation to marine mammals, therefore allowing the mean TL of catches to remain high.

37 2.4. Results

Model Simulations

Fitted Model: Past Scenario

Starting from the bottom of the food web, shifts caused by forcing functions were identified. Figure 2.8 (Past Scenario) identifies changes in the ecosystem using the fitted model with past sea ice, SST, and hunting data, as presented in % change from the starting 1970 biomass. Declines in ice algae and ice detritus of nearly 10% each, and increases in pelagic production (26%), and pelagic detritus (33%). Since both the ice algae and the pelagic production groups were forced, these changes were not surprising. Benthos which rely on energy transported from sinking particles, primarily ice algae (Wassmann, 1998; Lavoie et al., 2009), decline under conditions with less ice and ice algae. Zooplankton fare much better, with increases ranging from 12% (micro-zooplankton) to 58% (macro-zooplankton). Although zooplankton consume both ice algae and pelagic phytoplankton, biomass for these groups increases from 12% (micro-zooplankton) to 58% (macro- zooplankton), as the increases in pelagic production are high enough to compensate for the loss of ice algae in the diet.

38 DERYH WR WR

Polar Bear Foxe Bear Polar Bear Polar SH Whale Killer WHB Bear Polar seal Harp Seal Ringed Narwhal Sharks/Rays Seal Harbour W Beluga James Beluga Seal Bearded Seabirds E Beluga Cephalopods S Walrus Salmon Atlanc Bowhead N Walrus Char Arcc Gadiformes Fish Brackish Zoarcids Sculpins/ Capelin Sandlance Fish Marine Other Euphausids MacroZooplankton Echinoderms Crustaceans MesoZooplankton Other Worms Marine Bivalves Benthos Other Copepods MicroZooplankton Producon Primary Algae Ice Detritus Ice Detritus Pelagic WR

Fied Model ## WR

No Hunng ## EHORZ

Constant Climate ##

Figure 2.8: Change in biomass from 1970 value under various scenarios: Fitted Model (Fitted model driven using past climate and hunting trends), No Hunting (Model driven with past climate and no hunting), and Constant Climate (Model driven with 1970 climate repeated annually and past hunting). Functional groups are arranged by trophic level, from Foxe Basin polar bears (TL 4.92) to detritus (TL 1) 39 2.4. Results

Declines are identified predominantly in fish with benthic or epibenthic diets (Gadiformes: Arctic and Polar cod, Sculpins/Zoarcids: benthic fish, and sharks/rays) due to declines of ice detritus and other benthos. Gad- iformes and sculpins/zoarcids decreased in the diet of thick-billed murres an average of 68% and 57%, respectively, while pelagic-based fish show increases, with the largest being capelin and sandlance (figure 2.3). Fitting of time-series data (figure 2.5) from the diet of thick-billed murres appears to be unable to capture the full magnitude of the increase for both capelin and sandlance. Most marine mammal groups were fitted to data with the model replicating the trends observed. Polar bears, narwhal, and EHB beluga decline as expected. James Bay beluga, WHB beluga, and bowhead all show increasing trends as identified in model fitting. However, decreases are identified for southern walrus and bearded seals as hunting mortality impact their relatively small populations throughout the simulation. Northern walrus along with harp, ringed, and harp seals show increases in biomass, as hunting mortality is low relative to the population size, and there are decreases to predators (polar bears). The killer whale functional group biomass was based on sightings data (Higdon, 2007), therefore the biomass was not estimated by the model.

Other Scenarios: No Hunting and Constant Climate

Under the No Hunting scenario, all hunting and fishing mortality has been removed, while SST and sea ice were used as environmental drivers as in the past scenario. The biomass of all marine mammal groups increases, with the exception of western and James Bay belugas, which remain the same (figure 2.8). This is due to the relatively low hunting pressure on these specific groups, compared to their biomass. Lower trophic level organisms remain relatively unaffected, as climate is still driving the changes to these groups. Gadiformes are the only fish group to decrease further under this scenario indicating the abundance of marine mammals is causing high levels of mortality on this group.

40 2.5. Discussion

For the Constant Climate scenario, ice algae and ice detritus show increases compared to other scenarios as expected, however the biomass is quite similar to the 1970 value (<5% increase each), while pelagic production and pelagic detritus show slight declines (close to 10% decrease each). With- out the restriction on ice algae, caused by declining sea ice, these changes are propagated through the food web. Increases to benthos are observed as well as declines in zooplankton groups favoring a shift to a more benthic- dominated food web. Fish groups show increases from changes in the lower trophic levels, as well as predator release caused by hunting of marine mammals. Biomass for most marine mammal groups remains quite similar to the ﬁtted model indicating pressures from hunting are a more important factor in determining biomass than climate change.

2.5 Discussion

Fish Biomass and Changes to Fish Composition

While past commercial fishing endeavors have not been profitable (Stewart and Lockhart, 2005), it can be assumed that the region has modest fish biomasses, as Aboriginal communities have harvested fish for thousands of years. This is further corroborated by the ability of the ecosystem to sustain moderate biomasses of fish in the model. Estimates of fish for HB should be considered conservative, as the model only estimates enough fish to satisfy the diets of top predators and fishing, with a total fish biomass estimate of 3.42t · km−2 for 19701. The contribution of each functional group of fish is based on the diets of predators, and the minimum biomass required of each fish group to satisfy the needs of predators. Compared to other regions at similar latitudes this value is still low, but considering the low productivity of the ecosystem it can be considered a plausible estimate. In comparison, fish biomass estimates for other Ecopath with Ecosim models range from

1This is due to the EE parameter being set to 0.95 for ﬁsh species indicating nearly all mortality is caused by ﬁshing and food web interactions

41 2.5. Discussion

6.42t · km−2 to 49.62t · km−2 for other ecosystems at similar latitudes2. As HB is considered oligotrophic (Kuzyk et al., 2011), having a lower cumulative fish biomass than other similar latitude ecosystems is conceivable. Although there is a general lack of data on trends in fish species, the diet of thick billed murres provides insight as to potential changes occurring within the system. Most notably is the shift from a benthic-dominated system to a pelagic-based ecosystem, demonstrated in the diet of birds as they move from sculpins and zoarcids to pelagic sandlance and capelin (Gaston et al., 2003). Despite the fact that the model fits do not identify the exact patterns for the fish functional groups due to differences in data (figure 2.5)3, changes in composition of fish species are retained (figure 2.6). De- clines in the gadiform group stem from the declines in benthic species as prey items. Although the importance of epibenthic prey has been noted in the literature (Craig et al., 1982), in many regions copepods a predominant dietary staple (Sherwood and Rose, 2005). The model diet reflects a larger proportion of benthic prey items (see appendix A) facilitating the decline as climate warms. A re-analysis of the fitted model identifies less severe declines in the gadiform group with increased contribution of copepods and other zooplankton groups to the diet. However, crevasses within sea ice may be important areas for Arctic cod to areas to avoid predators (Gradinger and Blumm, 2004), therefore declines in sea ice would negatively impact Arctic cod. In light of this information, the gadiform group would be expected to decline as demonstrated within the model, albeit possibly with less severity. As the fish data are based on the northern edge of the model region, a greater understanding of fish distribution and diets is important to future modelling. In order to provide more accurate modelling of fish groups, large scale surveys of fish will be necessary for this region. In southern HB, fish may be impacted differently with large freshwater inputs from rivers, causing different environmental conditions.

2Fish biomass pertains to the cumulative biomass of all ﬁsh groups within the model. Values from other models at similar latitudes include; 1997 Icelandic shelf model (17.1t km−2) by Samb (1999), 1980 Bering Sea model (49.62t · km−2) by Trites et al. (1999), and a 1964 Ionian Sea model (6.42t · km−2) by Piroddi et al. (2010). 3Observed changes of ﬁsh groups are inputted as the contribution to bird diets

42 2.5. Discussion

Trophic Level of Catches

The ecosystem is able to sustain a higher mean TL of catches compared to the mean TL of the ecosystem, throughout the model simulation. Fish catches for 1970 totaled 1.14t for the whole model area, much lower than values reconstructed for the Canadian Arctic by Zeller et al. (2011). Per capita consumption rates used to estimate catches were obtained from the same source (Booth and Watts, 2007), however catch reconstruction included fish fed to dog sled teams, which the model presented does not. It is quite possible in reality that catches are higher than the values used in the model, current fishing mortality is low on fish groups indicating they would be able to sustain some increased level of fishing. While the constant TL of the ecosystem and catches would imply the ecosystem is stable both in its structure and in the composition of catches, it is uncertain if such a high trophic level of catches can be sustained in- definitely. For example, polar bear populations are shown to be declining within the model, and only under scenarios where harvest is included, indicating this level of harvest cannot be sustained. Future reductions of high TL species in the catch composition have the potential to reduce the mean TL of catches over time. This is consistent with ecosystems where fish species have been exploited, a term coined ”fishing down the food web” by Pauly et al. (1998a). Hudson Bay is one of a small number of ecosystems worldwide where marine mammal catches provide the greatest contribution to landings of all species, reflected in the high TL of catches. Ultimately, without reductions in catches, populations of marine mammals (such as polar bears) have and will likely continue to decline, thus reducing the TL of the ecosystem. Future simulations are necessary to determine if the current hunting and fishing pressures are sustainable.

Model Simulations

Model simulations (No Hunting, Constant Climate) identiﬁed expected responses in the model for most functional groups. Removal of hunting pressure causes increases in targeted species, with little eﬀects to lower trophic

43 2.5. Discussion levels. The ability of the ecosystem to withstand increases to the starting biomass ranging from ∼6% to >100% (for narwhal and northern walrus respectively), while not causing declines in fish populations, indicates the ecosystem could support larger biomasses of high trophic level species. Conversely, simulations with constant environmental drivers (sea ice and SST levels) indicate the sensitivity of producers and lower trophic levels to environmental changes. While producers are driven within the model, their responses mimic higher ice cover and lower SST. Although there are a multitude of factors contributing to primary production such as wind, temperature, light, snow cover, ice cover and nutrient input (Legendre et al., 1996; Gregg et al., 2003; Pabi et al., 2008), in the model higher ice cover facilitates an increase in ice algae, which allows for increased biomass of ice detritus. As this is the main source of detritus for benthos, there is an increase in all benthic groups and continued up to the benthic feeding fish. The decreases in pelagic production, which are assumed to be driven by SST, cause declines in zooplankton groups, and ultimately the fish feeding on zooplankton. The lack of response to higher level predators indicated that this scenario is not severe enough to cause changes to the ecosystem this far up the food web.

Conclusions

Bentho-pelagic coupling of sea ice to ice detritus may be an important factor in determining the abundance of benthic communities. Damaged algal cells from the sea ice sink faster than healthy ones, and increased flushing of algal cells caused by runoff through brine channels in the ice also increase exports (Tremblay et al., 1989). Export of ice algae to the benthic community was estimated at a minimum of 20% in southeastern HB (Tremblay et al., 1989). Moreover, accumulation of algal biomass within the sea ice is thought to favor an effective transfer to the benthos, as aggregated algal cells sink up to three times faster than individual algal cells (Riebesell et al., 1991). It should be noted that the timing of ice melt generally coincides with the pelagic bloom, making for a complex dynamic in bentho-pelagic coupling

44 2.5. Discussion

(Smith et al., 2006). The decrease in benthic groups that were observed in the model were impacted by declining sea-ice, yet there are certainly other important factors in the natural environment. In the model, these changes further explain the decreases in benthic fish (as reported from thick-billed murre diets). If the bentho-pelagic coupling was disrupted, it could allow for restructuring of the ecosystem where pelagic species would dominate lower trophic levels. Zooplankton may not continue to thrive under increasingly warming conditions. As the temperature increases, river runoff and freshwater inputs to the system are also expected to increase, causing both increased nutrients and increased stratification in the water column (Ingram et al., 1996). However, the impacts to the zooplankton community as a whole remain unknown. While ecosystems are resilient, restructuring will occur, potentially replacing marine mammals with larger fish populations. Through construction and simulations of the ecosystem model, changes to the HB ecosystem can be identified. Decreases to marine mammal populations combined with consistent harvest rates identify the threat to certain populations of top predators. Bottom up changes in SST and sea ice have only been demonstrated to impact the lower trophic levels indicating environmental changes are not yet severe enough to impact marine mammal populations. Even when considering polar bears, where a mediation function was included decreasing foraging habitat as sea ice declined (Stirling and Derocher, 1993). Yet, even with loss of foraging area, hunting mortality was responsible for larger declines in polar bears than climate change. While ecosystem TL remained relatively constant throughout the fitted model, catches for marine mammal groups (especially cetaceans) cannot be sustained over long periods of time. Certain populations of polar bears, narwhal, and beluga have shown declines causing some to be listed by CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) or COSEWIC (Committee on the Status of Endangered Wildlife in Canada), yet hunting rates have been relatively unaffected. This combined with more extreme future environmental changes, may cause a tip- ping point in the ecosystem. At some future point environmental changes

45 2.5. Discussion will be great enough to alter food webs, as will declines in top predators. While climate change is relatively unpredictable, it will likely cause future restructuring of lower trophic levels, and potentially the entire food web. Eﬀort to conserving marine mammal populations may prove useful in order to preserve ecosystem structure and prevent the potential over harvest of vulnerable species.

46 Chapter 3

Eﬀects of Harvest and Climate Change on the Antarctic Peninsula Marine Ecosystem (FAO area 48.1)

3.1 Synopsis

An Ecopath with Ecosim model was created for the Antarctic Peninsula (FAO area 48.1) in order to recreate the past changes to the ecosystem. Declines in krill (Euphausia superba) and increases in salps (Salpa thompsoni) were recreated based on past data trends, and environmental forcing. Through the use of environmental drivers; sea surface temperature, the Southern Oscillation Index and ice cover, and harvest records, trends from 1978 to 2009 were able to be captured within the model. Environ- mental variables were tested in diﬀerent combinations as drivers to assess which variables provided the best ﬁt to observed data. Sea surface temperature was selected over the Southern Oscillation Index as a model driver to producer due to a lower sum of squares value. Large increases in penguin colonies were unable to be captured by the model indicating food web changes do not cause the observed trends, and additional ecological information is needed. Overall declines in sea ice and krill (all life stages) cause large reductions across all trophic levels of the food web, reducing the biomass of nearly all species. Scenarios testing the model sensitivity to environmental drivers and harvest levels identify the ecosystems sensitivity to environmen-

47 3.2. Introduction tal changes. Increasing the past catches to the CCAMLR quota level results in minimal diﬀerences (>3% change in biomass) for all species groups when compared to the ﬁtted model using reported catches. Although krill harvest appears to have minimal impacts on the ecosystem within the model, the literature indicates harvest issues may be more sensitive to timing and location of catch rather than total removals.

3.2 Introduction

The Antarctic Peninsula, extending outside of the Antarctic Circle shows milder temperatures than the rest of the continent. It is also one of the fastest warming areas in the world, having an average sea surface temperature (SST) increase of 2.5◦C over the last 50 years (Marshall et al., 2006; Rogers et al., 2006), considerably higher than mean global increases (Anisimov et al., 2001; Hansen et al., 2006a). However, other areas of the Antarctic show mixed trends in SST changes, with some increasing and some decreasing (Turner et al., 2005). Polar areas are a major concern for envi- ronmentalists as warming will aﬀect ice dynamics, an important feature of high latitude ecosystems. Since the 1980s there have been many changes to the Antarctic Penin- sula, with the collapse of ice shelves as a result of warming. In addition to the breakup of ﬁve major ice shelves, warming temperatures have caused glacial retreat of some 244 glaciers over the last 50 years (Doake and Vaughan, 1991; Cook et al., 2005). Moreover, Antarctic ecosystems have a high number of endemic species (Kock, 1992), and their fate in relation to climate change is expected to be serious if these species can not adapt to thermal tolerances. Pole-ward migration is not an option for this region as land barriers prevent movement to higher latitudes. Krill attracts large quantities of top predators (Howard et al., 2004) and are considered to be a keystone species (Moline et al., 2000), linking most pathways in the food chain from primary producers to top predators. Species of seals, whales, penguins and migratory birds spend varying amounts of time in this region, but are all present in the summer months when pro-

48 3.2. Introduction ductivity is highest. All of these top predators are dependent upon Antarc- tic krill (Euphausia superba) at various degrees during the summer months (Doidge and Croxall, 1985; McConnell et al., 1992; Reid and Arnould, 1996; Casaux et al., 1997; Burns et al., 1998; Pauly et al., 1998b; Brierley and Reid, 1999; Tamura and Konishi, 2005). Changes to seal, bird, and baleen whale populations have been linked to changes in climate (Forcada et al., 2005; McMahon and Burton, 2005; Nicol et al., 2008), with the most likely reason being attributed to krill populations. In addition, krill from the Antarctic Peninsula are believed to be a source population to South Georgia (area 48.3), indicating the importance of krill not only to the peninsula, but to surrounding areas and predators at both those locations (Hofmann et al., 1998; Brierley et al., 1999). As krill survival is linked to sea ice through food and protection (Marschall, 1988; Daly, 1990), further warming and loss of sea ice has the potential to impact predators locally and across the Scotia Sea, causing uncertainty in the future of food webs. Summer abundance of krill in the South Atlantic is positively related to the sea ice extent in the previous winter (Loeb et al., 1997; Atkinson et al., 2004). Algae in the sea ice is an important food source for overwintering krill and new recruits in the spring, when algal biomass is released into the surface waters (Lizotte, 2001; Haberman et al., 2002). Ice is also thought to be used as a shield by krill to protect them from predators (Atkinson et al., 2004). Hence, declining sea ice could have a large impact on krill populations, by removing an important food source, and making the remaining krill more vulnerable to predators. Although krill are very important in the ecosystem, they can be out- competed in certain years by salps (Salpa thompsoni) (Loeb et al., 1997; Atkinson et al., 2004), whose abundance is favored by lower sea ice extent, warmer waters and low to moderate productivity (Nicol, 2006). In warmer years there is less ice algae available throughout winter for krill to graze on, and smaller spring blooms derived from the ice algae lead to poor reproductive success (Marschall, 1988; Loeb et al., 1997; Brierley and Watkins, 2000; Atkinson et al., 2004). In addition, salps are able to take advantage of lower production levels as they are eﬀective grazers, removing carbon from

49 3.3. Methods the surface and rerouting it to the benthos as faecal pellets (Bruland and Silver, 1981; Pakhomov et al., 2002; Dubischar et al., 2006). Krill on the other hand, are consumed by predators, thereby moving carbon up through the food chain. In addition, warmer, less saline conditions favor the growth of cryptophytes, a producer and important food source for salps (Moline et al., 2000). The only commercial fishery to remain open in the area at present is for krill. Currently, the krill fishery is open year round with an annual quota of 4 million tonnes for the Scotia Sea (620,000 tonnes for subarea 48.1), while in reality only about 100,000 tonnes are harvested each year primarily in the austral summer (Hewitt et al., 2002). Survival of fledging penguin chicks and Antarctic fur seals is lower in years of low krill abundance (Brierley and Reid, 1999), suggesting that in years of low abundance krill are not always available to predators. Results from a spatial model (Marin and Delgado, 2001) showed that roughly 80% of the krill catch was taken from within penguin foraging areas near the Antarctic Peninsula, suggesting fisheries are in direct competition with predators (Hewitt et al., 2002, 2004), potentially compounding the effects of already low krill biomass in some years. Small Scale Management Units (SSMU) have been considered to further divide the catches from area 48.1 into smaller management areas (Hewitt et al., 2004), however at present this has not been implemented into management (Flores et al., 2011). An ecosystem model was constructed for the Antarctic Peninsula to gain insight to the factors influencing the dominance of krill or salps, and the changes to the ecosystem which have occurred. The main objectives were to (i) establish a food web model, (ii) identify drivers for krill and/or salp dominance, and (iii) test the ecosystem effects of increasing the harvest of krill from current levels to quota levels.

50 3.3. Methods

Figure 3.1: Map of Antarctic Peninsula (FAO area 48.1) and surrounding areas. Other areas include South Orkney (48.2), South Georgia (48.3), South Sandwich (48.4), Weddell Sea (48.5) and Bouvet (48.6). The Scotia Sea represents areas 48.1, 48.2, and 48.3 combined.

3.3 Methods

Study Area

For the model the Antarctic Peninsula statistical area (FAO area 48.1) was selected as the model area (figure 3.1). The peninsula has a highly productive shelf zone (Smith et al., 1998) which attracts migratory species in addition to the year round inhabitants. It contains both continental shelf and deeper basin waters constrained by the ACC (Antarctic Circumpolar Current) which flows around the continent keeping cold water close to the shelf and warmer waters offshore. These highly productive waters encour- age high populations of krill biomass (Atkinson et al., 2008), which in turn draws high numbers of migratory species to the area selected. Selection of

51 3.3. Methods model area was chosen primarily based on available data. While the Belling- shausen Sea to the west has also demonstrated changes in climate (Abram et al., 2010), and South Orkney and South Georgia to the east are on the receiving end of krill being transported via currents (Hofmann et al., 1998), they were both excluded from the model. Data provided by CCAMLR is compiled by statistical area (CCAMLR, 2008a), and while high numbers of predators are present at South Georgia additional sub-Antarctic species groups would need to be included, thus expanding the food web further. The Bellingshausen Sea does not attract such high abundances of top predators as the peninsula, in addition to less data being available for this area. Thus, only subarea 48.1 was selected as the model area. All animals spending time in the region were included in the model. The model was selected to start in 1978. Krill and salp data were available starting from this time (Atkinson et al., 2004) in addition to penguin data for the 1980s (Fraser, 2006). While there was one year of reported catches for krill before 1978 (1976 389t), no indication of biomass was available pre- 1978, so this was selected as the starting year of the model.

Model Equations

Methods for creating the model were consistent with chapter 2, using the Ecopath with Ecosim approach (Christensen et al., 2005, 2007). Ecopath Equations 1 and 2, and Ecosim equation 3 remain unchanged from the previous chapter. The model was considered balanced when all Ecotrophic Efficiencies (EE) values were between 0 and 1. For the Ecosim portion the fitting of the model was accomplished through the inclusion of time series data (catches and abundance trends) in addition to environmental parameters; sea surface temperature (SST), ice cover and the Souther Oscillation Index (SOI). The automated fit to time series routine (Christensen and Wal- ters, 2004; Christensen et al., 2005) was used to assist in providing estimates of vulnerability parameters. A default value of 2 indicating a mixed relationship (neither top-down or bottom-up) was used to start the fit to time series routine. Low vulnerability values (close to 1) indicate a bottom-up

52 3.3. Methods relationship between predators and prey, while higher values (close to 100) indicate a top-down relationship. Once the automated search routine was completed, the vulnerabilities were further manipulated manually to lower the sum of squares (SS) value. The model was considered ﬁtted when further alterations to vulnerabilities or other parameters failed to reduce the SS value.

Model Inputs and Functional Groups

Input parameters for Ecopath were set to 1978 values for all species inhabiting area 48.1. 59 functional groups were created; 12 marine mammal groups, 5 penguin groups, 1 flying bird group, 12 fish groups, 15 benthic groups, 9 zooplankton groups, 4 phytoplankton groups and 1 detrital group. Migratory and year round residents of marine mammals were included in the model. Baleen whales spend their summer months in the model area, where they consume a large portion of their annual food intake (Best and Schell, 1996; Schell et al., 1989). In order to compensate for feeding outside the model area, the biomass of baleen whale groups was adjusted to 75% of their peak summer biomass to account for 75% of their annual food intake from within the model area, as migratory whales have been shown to feed outside of summer feeding habitats (Schell et al., 1989). Fish groups were separated based on feeding patterns, depth ranges, size and familial characteristics. Benthic groups were created based on abundances from survey samples and importance to predators. Zooplankton groups were divided into 4 krill groups (representative of different age classes), salps and various other zooplankton species important to krill and higher predator diets. The producers were split according to the conditions which favor them (warm years or cold years). One detritus group was created to represent all detritus. Appendix J provides a detailed description of all species groups. Primary producers were split into four groups in order to account for their different roles in the food web; ice algae, diatoms, cryptophytes, and other phytoplankton. Research has identified linkages between cryptophyte blooms and lower salinity water, as well as diatoms and higher salinity wa-

53 3.3. Methods ters (Moline et al., 2000, 2004). Diatoms and cryptophytes have been shown to be the dominant phytoplankton for the region in the summer months with diatoms having a strong association with sea ice (Varela et al., 2002; Gari- botti et al., 2003; Moline et al., 2004), thus demonstrating their importance to the food web. Diatoms are also favored in cooler years associated with higher sea ice, and are often an important component of sea ice algae, forming blooms at the ice edge when melting commences (Legendre et al., 1992). Ice algae remain in the sea ice overwinter and are utilized by predators such as krill throughout the winter (Marschall, 1988; Arrigo et al., 1997). Krill and salps were given their own functional groups within the model, with krill broken down into four levels each representing a different life stage (see krill summary in appendix J for further details). The krill stages chosen for the model were: (i) Krill eggs (eggs are spawned and sink to depths before ascending to reach food (Marr, 1962; Hofmann et al., 1992; Nicol et al., 1995; Reid, 2001)), (ii) larval krill (first feeding stage where food is critical, and the availability of phytoplankton is paramount (Ross and Quetin, 1986)), (iii) juvenile krill (physically resemble adults, but are sexually immature), and (iv) adult krill (sexually mature krill that are targeted by predators and the krill fishery (Lowry et al., 1998; Jones and Ramm, 2004)). One group was created for salps to represent solitary and colonial forms (Foxton, 1966). Surveys of benthos (Jazdzewski et al., 1986; Saiz-Salinas et al., 1998; Piepenburg et al., 2002) and fish (Daniels and Lipps, 1989; Frolkina et al., 1998; Kock, 1998; Arana and Vega, 1999; Barrera-Oro et al., 2000; Jones et al., 2000; Kock and Jones, 2005) provided samples from a variety of depths and areas. These were combined with peer-reviewed literature (appendix J) to obtain parameter values for benthic and fish functional groups. Penguin and marine mammal information for the entire region was scarce. However, surveys in time or space provided insights in population sizes and trends (Hunt, 1973; Gilbert and Erickson, 1977; Laws, 1977; Whitehouse and Viet, 1994; Boyd et al., 1998; Gelatt and Siniff, 1999; Leaper et al., 2000; Quintana et al., 2000; Branch and Butterworth, 2001; Fraser, 2006; Secchi et al., 2006; Williams et al., 2006). In many cases values from either the whole Antarctic or a small specific region of the model area were used to extrapolate values

54 3.3. Methods for area 48.1. Parameters for all functional groups within the model were set to values averaged over the entire model area. For area 48.1 the total area was set to 672,000 km2 (CCAMLR pers. comm. 2007). Biomass values (t · km−2) were taken from surveys and converted from abundance using average weights, and total model area if necessary. Production and consumption parameters (P/B and Q/B) were calculated from empirical equations or taken directly from literature if available. These required species specific equations found in appendix J, calculated as annual values (y−1). Diets were available from literature for most species. On occasion parameter values were inferred from similar species, or when values could not be reasonably estimated. The Ecotrophic Efficiency (EE) was left blank for most groups to be estimated by the model. Ad- justments to the model during the balancing and fitting processes primarily included changes to diet, with some adjustments to P/B, Q/B. The model was considered balanced when all EE values were between 0 and 1 (Chris- tensen et al., 2005). For a detailed description of the model parameters and calculations please refer to appendix J.

Table 3.1: Time series data used for model ﬁtting. Data sources and type of data used is noted.

Time series data Type of data used Reference Krill Abundance Relative Abundance Atkinson et al. (2004) Krill Catch Forced Catches CCAMLR (2008a) Krill Effort Effort CCAMLR (2008a) Salp Abundance Relative Abundance Atkinson et al. (2004) Other Fishery Catch Forced Catches CCAMLR (2008a) Other Fishery Effort Effort CCAMLR (2008a) Adelie Penguin Abundance Relative Abundance Fraser (2006) Gentoo Penguin Abundance Relative Abundance Fraser (2006) Chinstrap Penguin Abundance Relative Abundance Fraser (2006)

The model fitting process in Ecosim incorporated catches of krill and fish (figure 3.2) from CCAMLR data ranging from 1978-2007 (CCAMLR, 2008a), along with environmental drivers including sea ice, SOI and SST in an attempt to recreate changes recorded in the past. A summary of all abundance trend data used for the model fitting for functional groups is provided in table 3.1. Sea ice and SST data were extracted from the HadISST global

55 3.3. Methods model by the British Atmospheric Data Centre (BADC, 2010), while the SOI was taken from the PALMER LTER (Long Term Ecological Research) (Stammerjohn, 2007). The ice cover forcing function (FF) was used as a driver for the ice algae and diatom functional groups. Ice algae remain in the sea ice overwinter and are utilized by predators such as krill throughout the winter (Marschall, 1988; Arrigo et al., 1997). Diatoms are favored in cooler years associated with higher sea ice, and are often an important component of sea ice algae, forming blooms at the ice edge when melting commences (Legendre et al., 1992). In addition sea ice was used as a FF for ice algae predators, applied to the arena area for each predator. The ecological interpretation is that as ice cover increases, so does the arena area for predators to feed on ice algae. SOI4 and SST were used under different fitting attempts (A and B respectively) for cryptophytes and the other phytoplankton functional groups. This is due to cryptophytes having higher biomass in warmer years (Moline et al., 2004), and the other phytoplankton group representing species associated with the spring bloom. The annual SST pattern has a similar pattern to summer blooms during ice free conditions. FF of cryptophytes and other phytoplankton were chosen to help fit the salp functional group, which is able to tolerate warmer water than krill (Atkinson et al., 2004). The SOI was tested in the model as salps abundance has been linked to the SOI (Loeb et al., 2009). Mediation functions were also used in the fitting of the model to represent indirection interactions between species groups. For example, as krill have been observed by SCUBA divers to retreat into crevasses in sea ice for protection (Marschall, 1988), a mediation function between krill and their predators was created. As the biomass of ice algae increases, krill become less vulnerable to their predators, with a large decline as ice decreases from

4The Southern Oscillation Index (SOI) used in the model is calculated using the diﬀer- ence in air pressure between Tahiti and Darwin, Australia. Positive values indicate cold ocean temperature, higher air pressure in Tahiti, and lower air temperature in Darwin. Negative values indicate, lower air pressure in Tahiti, higher air pressure in Darwin, and warmer waters. Positive values are generally associated with La Nina years, while negative values are associated with El Nino years.

56 3.3. Methods

50 12 45 Fish Krill 10 40 35 8 30 25 6 20 4 15

Fish Catch Catch Fish tonnes) 1000 ( 10 2 catch tonnes)Krill (10000 5 0 0 1974 1979 1984 1989 1994 1999 2004

Figure 3.2: Krill and fish catches presented by year. Data was extracted from the CCAMLR online database (CCAMLR, 2008a). the starting values within the model, and tapering impacts from low to extremely low ice cover5. This mediation function was applied to both the larval and juvenile stages of krill under both fitting scenarios (SOI and SST). As salps are pelagic organisms with the abundance higher in warmer years with lower sea ice (Moline et al., 2004; Nicol, 2006), the mediation function used indicated as sea ice decreased (as determined by ice algae), the foraging area of salps increased using a linear relationship 6. This mediation function was applied to all prey groups of salps under both fitting scenarios (SOI and SST). Other environmental time series were tested in the fitting of the model, but did not produce optimal results. Data from the PALMER LTER study of sea ice extent, open water extent and air temperature were considered (Stammerjohn, 2007). Selection of the fitted model was based on the lowest sum of squares (SS) value in addition to the model representing past data.

5This interaction was ﬁt to a sigmoid curve. Figure and starting points are shown in appendix J 6See appendix J for ﬁgure

57 3.3. Methods

While sea ice extent did provide comparable results to the ice cover FF (forcing function) (once both FF were rescaled to average 1 for the first year), future data is available for percentage ice cover, therefore it was selected over ice extent. In addition, a clogging function for salps was employed to reflect the clogging of mucous nets in areas of high particle concentration. This has been shown to occur for some species of salps in lab experiments, and is believed to be the cause of a mass stranding of Salpa thompsoni near the Antarctic Peninsula in 2002 (Harbison et al., 1986; Pakhomov et al., 2003). The mediation function was applied to the search rate of salps on other phytoplankton and cryptophytes, so as the biomass of other producers increase, the search rate will also increase to a certain point and then drop off. While this mediation function did improve the SS value initially, the sea ice mediation function provided a lower SS value. The combination of both mediation functions of salps did not provide a lower SS value than the sea ice mediation function alone, so the clogging function was removed from the model.

58 45 3.5 30 Ice Cover 40 Temp 3 20 35 2.5 10 30 2 25 0 1.5 20 -10

Ice CoverIce (%) 1 15

Temperature(Deg C) -20 10 0.5

Southern Oscillaon SouthernOscillaon Index(SOI) -30 5 0 0 -0.5 -40 J F M A M J J A S O N D 1978 1983 1988 1993 1998 2003 2008

Figure 3.3: Environmental drivers used in the model ﬁtting process. Sea surface temperature (SST) and sea ice are presented as mean values with 95% CI to show annual patterns. Southern Oscillation Index (SOI) is presented as a timeseries to identify positive and negative years. 59 3.4. Results

Model Analysis and Simulations

Once the model was fit to data, Monte Carlo simulations were used to estimate the range of acceptable input parameters, specifically biomass. Equa- tion 2.4 from chapter 2 was used in the same manner as for the Hudson Bay model. The Ecopath input value for each functional group was used as the mean value with the CV (Coefficient of Variation) values for biomass parameters presented in table 3.2. CV values were determined by pedigree ranking routing, whereby uncertainty in input parameters is determined based on the quality or source of the value (Christensen et al., 2005). Mean trophic level (TL) of the ecosystem, and catches were calculated using equations 2.6 and 2.7 from chapter 2. Producers and detrital groups are set to a TL of 1, with the rest of predators calculated based on diets. Two models using SST and SOI to drive the warmer phytoplankton species (cryptophytes and other phytoplankton) showed comparable results for most species groups. However, the SST model was ultimately selected and used for simulations. Simulations testing the model sensitivity to drivers were applied to the fitted model. The first ”Constant Climate” scenario tested the sensitivity of the model to climate drivers, by removing changes throughout time. The SST and ice cover patterns from 1978 were replicated to mimic constant climate over time. Under this scenario hunting trends were maintained to represent past catches. Next a ”Harvest Quota” scenario applied the krill quota as the level of catch for krill each year of the simulation in combination with past environmental drivers. While the krill fishery operates lower than the quota limit of 625,000 tonnes for area 48.1 (Hewitt et al., 2002), the potential effects to the ecosystem if the fishery had operated at quota levels of harvest were explored.

3.4 Results

Ecopath Output

In the Ecopath phase, changes were made to parameters in order to ensure the model could be balanced before moving onto the Ecosim portion.

60 3.4. Results

General changes to Ecopath parameters were made in order to balance the model. Calculated consumption rates of marine mammals were high in some cases and had to be decreased to prevent EE for prey groups from going over 1. In most cases the Q/B value reduction was small (less than 10% of the initial value). The P/B ratios for fish groups calculated by equations in Pauly (1980) were too low. As the empirical data used to formulate this equation was based on temperate and tropical fish species and excluded polar data, it most likely underestimates the value for polar species (Palomares and Pauly, 1998). P/B values were increased to balance the fish groups and the rest of the model. Next, literature indicates a very strong dietary link between predators and krill. However, even though krill biomass was large in comparison to other zooplankton (48% of total zooplankton biomass including salps for all stages of krill), the contribution to the diet of predators had to be decreased in order to balance the model. Finally, changes were made to the P/B and Q/B values for invertebrates. Most alterations made to calculated values were increases in order to balance the model, to provide enough prey for fish and pinniped groups. In addition to these general parameter changes, smaller adjustments were made to diets in the fitting process to better capture past trends. Final model parameters available in appendix J are presented as the values used for the balanced model, with parameters adjusted during the fitting process indicated the table 3.2.

61 Table 3.2: Balanced Ecopath model parameters. Biomass (B) and catches are presented in t · km−2, PB (Produc- tion/Biomass ratio), QB (Consumption/Biomass ratio), and BA (Biomass Accumulation) are presented in y−1. EE (Ecotrophic Eﬃciency) and P/Q (Production/Consumption) ratios and TL (Trophic Level) are dimensionless. Bolded values are estimated by the Ecopath model. The CV (Coeﬃcient of Variation) values were used to calculate biomass ranges for Monte Carlo routine.

Group Name TL B PB QB EE PQ BA Catches CV

Killer Whales 4.543 0.001 0.050 11.000 0.000 0.005 - - 0.7 Leopard Seal 4.139 0.006 0.120 8.100 0.637 0.015 - - 0.7 Ross Seal 4.123 0.004 0.130 15.300 0.830 0.008 - - 0.4 Weddell Seal 3.972 0.021 0.170 13.880 0.689 0.012 - - 0.7 Crabeater Seal 3.423 0.164 0.090 15.860 0.363 0.006 - - 0.7 Antarctic Fur Seals 3.694 0.028 0.175 25.000 0.862 0.007 - - 0.7 S Elephant Seals 4.250 0.006 0.165 10.370 0.437 0.016 - - 0.7 Sperm whales 4.203 0.005 0.034 7.330 0.000 0.005 - - 0.7 Blue Whales 3.410 0.001 0.032 3.530 0.683 0.009 - - 0.7 Fin Whales 3.441 0.003 0.035 4.120 0.524 0.008 - - 0.7 Minke whales 3.270 0.065 0.064 6.340 0.910 0.010 - - 0.7 Humpback whales 3.343 0.020 0.040 4.120 0.963 0.010 - - 0.7 Emperor penguins 3.871 0.005 0.150 28.690 0.933 0.005 - - 0 Gentoo Penguins 3.930 0.007 0.220 29.000 0.642 0.008 - - 0.7 Chinstrap Penguins 3.917 0.005 0.330 34.000 0.696 0.010 0.057 - 0.7 Macaroni Penguin 3.670 0.014 0.300 25.000 0.373 0.012 0.100 - 0 Adelie Penguins 3.518 0.034 0.290 30.000 0.793 0.010 - - 0.7 Flying birds 3.697 0.190 0.340 14.880 0.950 0.023 - - 0.4

Continued on Next Page 62 Table 3.2 Continued

Group Name TL B PB QB EE PQ BA Catches CV

Cephalopods 3.404 2.490 0.950 2.000 0.653 0.475 - - 0.4 Other Iceﬁsh 3.689 0.337 0.380 1.570 0.726 0.242 - 1.00E-05 0.7 Toothﬁsh 4.228 0.046 0.165 0.770 0.627 0.214 - 1.00E-05 0.7 Lg Nototheniidae 3.335 0.590 0.370 1.950 0.452 0.190 - 1.00E-05 0.7 Sm Nototheniidae 3.332 0.341 0.650 2.200 0.873 0.295 - 1.00E-05 0.7 Shallow Demersals 3.375 0.031 0.750 4.125 0.362 0.182 - - 0.7 Deep demersals Lg 3.684 0.042 0.290 2.180 0.803 0.133 - - 0.7 Deep demersals Sm 3.687 0.080 0.650 2.700 0.820 0.241 - - 0.7 Myctophids 3.263 0.185 1.350 3.730 0.882 0.362 - 1.00E-05 0.7 Other Pelagics 3.776 0.490 0.550 2.020 0.838 0.272 - 1.00E-05 0.7 C gunnari 3.391 0.290 0.480 1.800 0.475 0.267 - 1.00E-05 0.7 P antarcticum 3.269 1.250 1.100 3.550 0.603 0.310 - 1.00E-05 0.7 N gibberifrons 3.199 0.810 0.410 1.550 0.645 0.265 - 1.00E-05 0.7 Mollusca 2.129 9.500 0.639 2.556 0.608 0.250 - - 1 Salps 2.227 8.000 10.000 33.333 0.010 0.300 - - 1 Urochordata 2.135 5.050 0.234 1.000 0.554 0.234 - - 1 Porifera 2.000 12.719 0.159 0.795 0.815 0.200 - - 1 Hemichordata 2.000 0.045 0.375 2.000 0.534 0.188 - - 1 Brachiopoda 2.158 0.028 0.898 4.500 0.590 0.200 - - 1 Bryozoa 2.108 0.491 0.475 1.750 0.980 0.271 - - 1 Cnidaria 2.438 1.531 0.250 1.000 0.982 0.250 - - 1 Crusteceans 2.374 3.613 1.050 4.200 0.888 0.250 - - 1 Other Arthropods 2.929 1.010 0.616 3.326 0.981 0.185 - - 1 Worms 2.438 12.000 0.700 3.200 0.840 0.219 - - 1

Continued on Next Page 63 Table 3.2 Continued

Group Name TL B PB QB EE PQ BA Catches CV

Echinoidea 2.732 4.330 0.116 0.464 0.774 0.250 - - 1 Crinoidea 2.428 0.164 0.125 0.800 0.523 0.156 - - 1 Ophiuroidea 2.479 6.760 0.450 1.800 0.551 0.250 - - 1 Asteroidea 2.345 1.778 0.231 0.924 0.774 0.250 - - 1 Holothuroidea 2.000 5.450 0.316 1.100 0.938 0.287 - - 1 Krill Adult 2.529 9.080 1.500 33.000 0.672 0.045 - 0.055 1 Krill Juvenile 2.250 25.260 0.900 49.481 0.788 0.018 - 0.018 1 Krill Larvae 2.000 0.879 2.500 149.443 0.011 0.017 - - 1 Krill Embryo 2.000 0.006 8.000 698.506 0.237 0.011 - - 1 Macro-Zoopl 2.154 8.170 7.577 25.257 0.950 0.300 - - 0.7 Micro-Zoopl 2.000 2.900 65.000 110.000 0.982 0.591 - - 0.7 Cryptophytes 1.000 2.200 80.000 0.000 0.983 - - - 0.4 Copepods 2.150 15.200 26.066 50.000 0.950 0.521 - - 0.7 Diatoms 1.000 17.410 90.510 0.000 0.396 - - - 0.7 Ice algae 1.000 25.000 45.000 0.000 0.874 - - - 0.7 Other Phytopl 1.000 5.500 105.000 0.000 0.806 - - - 0.4 Detritus 1.000 3.430 - - 0.176 - - - -

Continued on Next Page 64 3.4. Results

Ecosim Fitting

The model was fit under 2 conditions: The first fitting used SOI to drive cryptophytes and the other production group, and the second fitting used temperature (SST) as a forcing function for cryptophytes and other production. Both fitting scenarios used ice cover (% of model area covered with ice) as a FF for the ice algae and diatom functional groups. In addition, both fitted models incorporated mediation functions allowing young krill (larval and juvenile stages) increased protection from predators at higher sea ice concentrations, and salps a smaller foraging area as sea ice increases (please refer to appendix J for a full description of forcing and mediation functions). For both attempts at fitting the model, there was no difference to the fit of penguin groups. Declines in Adelie penguins were captured through the decline of the main prey item krill. For the chinstrap and gentoo penguins, obtaining increases in the population while food sources (krill, cephalopods, and fish) declined was not possible. However, based on increases in both populations documented, biomass accumulation rates of 0.10y−1 and 0.057y−1 were added for chinstrap and gentoos respectively. For gentoo penguins this was based on increases of 5.7% at Cierva Point on the Antarctic Peninsula, and a nearly 50 fold increase at PALMER Station on Anvers Island (Quin- tana et al., 2000; Fraser, 2006). Chinstrap penguin trends identify increases in breeding pairs from 28 to 1288 between 1996-2004 at PALMER Station (Fraser, 2006) and increases in colonies ranging from 6-10% per year for specific areas within the model area (Fraser et al., 1992). However, these trends of large increases are not indicated to hold true outside of these specific study sights.

65 SOI Fitted Model

Chinstrap Penguins Adelie Penguins Gentoo Penguins

Adult Krill Salps

SST Fitted Model

Chinstrap Penguins Adelie Penguins Gentoo Penguins

Adult Krill Salps

Year 66 Figure 3.4: Model fitted to data for the Antarctic Peninsula using data trends from table 3.1. Data for the SOI fitted model (top) and the SST fitted model (bottom) remain the same, only different forcing functions were used as model drivers. 3.4. Results

Krill were fit to the model using the mediation function for sea ice (see appendix J), and through the use of sea ice as a driver of their main food sources, sea ice algae and diatoms in addition to protection from predators. Krill abundance has been shown to be higher in years with lower sea temperature, higher sea ice extent, and higher nutrient concentrations, while the opposite patterns are observed for salps (Lee et al., 2010). Although the peak in biomass for 1983 was not captured in the model for adult krill, juvenile krill show a higher biomass than adult krill in this year. While some juvenile krill are likely caught in the samples provided by this dataset, as the adult group is classified by sized 35mm and larger, neither group shows the highest biomass in this year. The highest adult krill biomass is shown in the model for 1992 at just over 23t·km−2 while the highest biomass for juveniles was in 1988 at just over 58t · km−2, the highest biomass projected by the model for any krill group. Krill trend data from observations indicates high biomass in 1992 and 1996, although adult krill in the model does not show high biomass in these years. Juvenile krill shows a relatively high biomass in 1996, but not 1992. The greatest differences between the two fitted models arises from the groups where SST and SOI were used as forcing functions: cryptophytes and the other phytoplankton group. For cryptophytes, both models show peaks in abundance in 1987 and 1992, however values are higher under the SST fitted model. The other phytoplankton group shows the same general trends for both fitted models, however peak abundances are higher under the SST fitted model, and low values are more extreme under the SOI fitted model. These differences influence the salp group which shows different trends under both fittings. Under the SST fitted model a peak in salp biomass for 1989 is lower than for the SOI fitted model. Also the SOI fitted model generally has higher salp biomass values after 1999 compared to the SST fitted model. The ending biomass for the SOI fitted model is higher for the salp group. While the SOI fitted model visually appears to fit the salp trend data better, it has been suggested recently in the literature that salp trends from 1998 onward are thought to have stabilized showing mid range abundances in recent years when compared to data from 1975-2002 (Lee et al., 2010). This

67 3.4. Results is different from the data used for the model (Atkinson et al., 2004) which still shows fluctuations in salp biomass past 1998 (figure 3.4 data points). Krill and salp abundance is thought to be strongly influenced by the SOI, the ACW (Antarctic Circumpolar Wave) which brings cold deep water the surface at the peninsula, and the placement of the sACCf (southern Antarc- tic Circumpolar Current Front) (Lee et al., 2010). Salp abundance has been shown to have a strong negative correlation to sea ice extent in the previous winter, which is negatively correlated to SOI (Loeb et al., 2009). SST was tested to fit the model as it is a contributing factor to both the ACW and sACCf, although there are many other important factors contributing to the dynamics of these environmental drivers. Sum of squares (SS) value for the SST fitted model was 68.57, and SS for the SOI fitted model was 78.95. With biomass trends for most species being similar (see appendix P for graphs of all functional groups), it was decided that the SST driver provided a better fit based on SS values.

Model Results

Estimates of all parameters in the Monte Carlo routine (figure 3.5 with CV values in table 3.2) from 1000 iterations were unable to improve SS value, however they did provide ranges of acceptable input parameter values. In general, the model was able to support a larger range of biomass for marine mammal species with higher initial biomasses (weddell seals, crabeater seals, fur seals, minke whales and humpback whales). Ranges for penguin groups were relatively low, although the model is able to support a much higher biomass of flying birds. Fish groups share the same CV value, with the general trend of biomass range proportional to starting value. P. antarcticum and N. gibberifrons have the largest starting biomasses and the largest range of acceptable biomasses, likely due to their importance to predators diets. Demersal fish (shallow and deep groups) and toothfish, show very narrow ranges of biomass. The largest biomass ranges for benthic invertebrates are for sponges and worms, which have the largest biomasses in surveys (Jazdzewski et al., 1986; Saiz-Salinas et al., 1998; Piepenburg et al., 2002).

68 3.4. Results

Marine Mammals and Birds Fish

0.30 2.00

1.75 0.25

1.50 0.20 1.25

0.15 1.00

0.75 Biomass (t/km2) Biomass 0.10 (t/km2) Biomass

0.50 0.05 0.25

0.00 0.00 Ross Seal Toothsh Fin Whales C. gunnariC. Flying birds Flying Myctophids Blue Whales Other Icesh Killer Whales Killer Leopard Seal Weddell Seal Weddell P. antarcticum P. Minke whales Minke Sperm whales N. gibberifrons N. Other Pelagics Crabeater Seal Crabeater S Elephant Seals S Elephant Adelie Penguins Antarctic Fur Seals Humpback whales Humpback Gentoo Penguins Gentoo Emperor penguins Emperor Deep demersals Lg demersalsDeep Macaroni Penguin Macaroni Deep demersals Sm demersalsDeep Small Nototheniidae Small Shallow Demersals Shallow Large Nototheniidae Chinstrap Penguins Chinstrap

Invertebrates Plankton Groups

15 40

12 30

25 9

6 15 Biomass (t/km2) Biomass Biomass (t/km2) Biomass

10 3 5

0 0 Salps Diatoms Worms Ice algae Porifera Bryozoa Mollusca Cnidaria Krill Adult Copepods Crinoidea Asteroidea Echinoidea Krill Larvae Krill Embryo Cephalopods Krill Juvenile Ophiuroidea Urochordata Cryptophytes Brachiopoda Crusteceans Hemichordata Holothuroidea Arthropod Other Micro−Zooplankton Macro−Zooplankton Other Phytoplankton Figure 3.5: Monte Carlo estimates of biomass using CV values from table 3.2

69 3.4. Results

Copepods have the largest range of biomass for zooplankton groups, with juvenile krill and macro-zooplankton having the next largest ranges. Salps in comparison to other zooplankton have a narrow range of acceptable starting biomass indicating the model cannot support a large starting biomass of salps, although the ﬁtted model indicates higher biomasses are supported throughout the last 30 years. Results suggest that the model can support higher biomasses of diatoms and ice algae, with lower biomasses of warmer water associated producers (cryptophytes and other producers).

The trophic level of catches (TLC ) declines from an initial value of 3.39 in 1978 to 2.34 in 2007. This is due to the catch being largely comprised of fish species in early years of the model (figure 3.2), as test fisheries operated in the early 1970s in the model area (CCAMLR, 2008b). This combined with low krill catches in early years resulted in a higher TL of catches than in later years where krill dominate, as krill are at a lower trophic level than fish species. Trophic level of the ecosystem (TLE) maintains a relatively stable trend, hovering around a TL of two, denoting there is a large proportion of the ecosystem biomass at lower trophic levels. While ecosystem TL appears to indicate the ecosystem maintains stability, total biomass shows declines from 209t·km−2 in 1978 (mean=206 from 1978-1982) to an ending value of 135t·km−2 in 2007 (mean=159 from 2003-2007).

Table 3.3: Trophic level of the ecosystem TLE and catches TLC along with cumulative biomass of the ecosystem t·km−2 presented in 10 year increments throughout model simulation. Data presented are calculated from the SST ﬁtted model.

Year TLE TLC Biomass 1978 1.91 3.39 209.73 1988 2.07 2.30 186.59 1998 2.01 2.28 156.75 2007 1.98 2.34 134.97

70 71 hnei ims rmtesatn 17)vle nigboasi acltda h enfo 0220 to 2002-2007 from years. mean (%) cool the or percent as warm the calculated to as is due presented biomass levels are trophic Ending Values lower value. in scenarios. (1978) biomass other of starting and exaggerations the model avoid from ﬁtted biomass the in for change results Simulation 3.6: Figure Krill Quota Harvest Krill Quota SOI Model Model SOI Model Constant Climate Constant SST Model fitted

Killer Whales S. Elephant Seals Toothfish Sperm whales Leopard Seal Ross Seal Weddell Seal Gentoo Penguins Chinstrap Penguins Emperor penguins Other Pelagics Flying birds Antarctic Fur Seals Other Icefish Deep demersals Sm Deep demersals Lg Macaroni Penguin Adelie Penguins Fin Whales Crabeater Seal Blue Whales Cephalopods C. gunnari Shallow Demersals Humpback whales Lg Nototheniidae Sm Nototheniidae Minke whales P. antarcticum Myctophids N. gibberifrons Crusteceans Worms Krill Adult Crinoidea Other Arthropods Bryozoa Echinoidea Cnidaria Ophiuroidea Krill Juvenile Holothuroidea Hemichordata Macro-Zoopl. Cryptophytes Salps Mollusca Brachiopoda Urochordata Porifera Asteroidea Krill Larvae Krill Embryo Micro-Zoopl. Copepods Diatoms Ice algae Other Phytopl. Detritus -50.1% to - 75% -50.1% to -25.1% to -50% to -25.1% 25.1% to 50% 25.1% to 75% 50.1% to 0.1% to 25% to 0.1% -25% to 0% -25% to < -75.1% < 75.1% > 3.4. Results

Constant Climate and Increased Harvest Scenarios

For hypothetical scenarios testing the model sensitivity to harvest levels and environmental drivers, the SST fitted model was used. Ending values were calculated as the 5 year average biomass from 2003-2007. Under the ”Constant Climate” scenario, environmental trends (SST and ice cover) were repeated as monthly values for 1978, the first year of the simulation. While this retained seasonal patterns, the annual averages remained constant, and harvest patterns of the past remained intact. Total primary production still declines in this scenario, although less than 5%. Cryptophytes, diatoms, and ice algae all decline less than 10% due to top down effects, while the other primary production group increases less than 10%. Detritus decline is less than 3%. Although the biomass of detritus and primary production does decline even with constant climate, there is still enough biomass of these groups to result in increased biomass for the ecosystem, and most functional groups and the total ecosystem biomass. Total biomass of the ecosystem increased to 217.9t·km−2, indicating that if environmental drivers (via primary production) remained constant, the ecosystem could support a total higher biomass than 1978 values. All marine mammal groups increase with values ranging from 17.23% (weddell seals) to 6.52% (minke whales). Impacts to penguins and flying birds were all positive, with chinstrap penguins increasing the most at 20.37%, and emperor penguins at 11.85%. Fish groups increased from 12.26% (other pelagics) to 23.02% shallow demersals. Invertebrate groups show mixed results with urochordates, bryozoans, and cnidarians each decreasing near 5%, while the rest of the invertebrate groups (excluding zooplankton groups) increase up to 20.02% for holothuroideans. For zooplankton functional groups, copepods, micro-zooplankton, macro-zooplankton, krill embryo, and juvenile krill all declined with values ranging from 0.23% (krill larvae) to 10.63% (copepods). For zooplankton groups that increased, values ranged from 5.79% (juvenile krill) to 16.57% (salps). The total biomass of all krill groups combined increased by 6.42%. For the ”Increased Harvest” scenario, krill catches were forced at quota

72 3.5. Discussion levels to assess the impacts on the ecosystem if krill had been harvested at the quota maximum throughout the past. Environmental drivers with past trends of decreasing ice and increasing SST were used in this scenario. Results for this scenario are very similar to the past trends, indicating increasing harvest rates would not greatly alter the ecosystem structure. Dif- ferences between the increased harvest scenario and the SST fitted model were minimal; all functional groups showed less than ±3% difference in ending biomass between the models. Of the krill functional groups, adult krill had the largest difference declining a further 2.16% under the increased harvest scenario to show a total decline of 35.61%.

3.5 Discussion

Balancing of the Ecopath model indicated contributions of krill to the diets of predators was higher than the Ecopath model initial biomass of krill could support (see appendix J for diet descriptions). Possible explanations for this include overestimation of krill in the literature as a dietary component, higher biomass of krill than initial parameterization of the Ecopath model, or contributions of krill to predator diets from outside the model area. While diet studies are primarily based in the austral summer where there is greater access to the region, samples of predators stomach contents are likely to overestimate the importance of krill as they are more available during the spring and summer months. Some studies have accounted for the diets of seals and penguins during the winter, establishing the importance of other non-krill prey items to predators diets, namely ﬁsh, cephalopods, and other zooplankton species (Green, 1986; Whitehouse and Viet, 1994; Reid, 1995; Kirkwood and Robert- son, 1997; Lowry et al., 1998; Clausen and Putz, 2003). Studies suggest myctophids may be a key energy-rich dietary component for lactating fur seal and some stages of chinstrap penguins which may rival krill as an energy source in Antarctic ecosystems (Ichii et al., 2007; Flores et al., 2008). When only summer diets were available for species within the model, care

73 3.5. Discussion was taken to include prey items for winter feeding, should this be an issue7. Yet, even with a substantial annual biomass of krill in the Ecopath model 37g · m−2, diets of predators had to be altered from compositions suggested in the literature to be less heavily weighted on krill. This is perhaps due to a lack of understanding of winter diets and highlights the need for more winter based diet studies of krill predators. Additionally, it could be explained in the model through a higher growth rate (or an increased P/B ratio in the model). P/B values were taken from krill studied in the Cooperation and Cosmonaut Seas, which may be higher in the coastal areas of the Antarctic peninsula (Atkinson et al., 2008). Increasing the P/B value in the initial Ecopath model would allow for higher contributions to the diets of predators. It is also possible the total biomass of krill present in the ecosystem at the time of the Ecopath model was underestimated by Atkinson et al. (2004). With an increased biomass in the Ecopath model, it may be possible there would be enough krill available to support higher contributions to the diets of predators. Of the Ecosim models the fitted model using SST was selected, as it provided a lower SS. Although the salp trend is not captured in more recent years (1998 onwards), recent evidence suggests there may have been a leveling out of salp abundance resulting in smaller fluctuations than the data suggests. Salp populations have been more consistently present in the shelf portion of the Antarctic Peninsula since 1999, with a strong negative correlation to the number of ice days in the previous year (Ross et al., 2008), lending credibility to the theory of salp populations leveling out after 1998 (Lee et al., 2010). The model fitted with SOI on producers provides a better fit to salps as it captures the more extreme fluctuations in later years (after 1998). However, the inclusion of the salp mediating function for the SST driven model lowers the SS value below that of the SOI driven model. While other drivers were tested (sea ice extent, air temperature and open water extent) there is the potential that better drivers exist. The movement of the Antarctic Circumpolar Wave (ACW) and the placement of the

7For example this is not an issue for baleen whales whom are only present in the summer months and feed almost exclusively on krill and other zooplankton.

74 3.5. Discussion southern Antarctic circumpolar current (ACC) front is believed to influence krill and salp abundance along with the SOI (Lee et al., 2010). The clima- tologies associated with krill and salp locations were not correlated (Ross et al., 2008), indicating environmental factors for each may not be mutually exclusive. While in other Antarctic areas spatial overlap of krill and salps is not common, at the peninsula the southern boundary of the ACC is relatively close to the shelf of the continent (Ducklow et al., 2007), promoting greater mixing and possibly pushing more suitable salp conditions closer to the peninsula. However, long-term, standardized datasets were not available for modeling purposes, but should be explored in the future. Differences between the two model fits show changes to the ending composition of primary producers. However, total production biomass remains fairly constant. Cumulative biomass for all primary producer starting groups was 53.50t·km−2 in 1978, with the ending value for SST fitted model at 32.01t·km−2 and 31.34t·km−2 for the SOI fitted model. The other phytoplankton group and cryptophytes show the largest changes between the two models with biomass demonstrating similar trends between the two models, and variance caused by the drivers affecting inter-annual variability (appendix P). Overall the decrease in primary production of nearly 40% (for all producer group biomass combined) is much higher than the 10% decline estimated from Gregg et al. (2003). Although it should be noted that the decline of 10% is from satellite data and excludes the contribution of ice algae to total production (Gregg pers. comm), meaning the declines are based primarily on summer bloom values. Chl a concentrations from satellite models from 1975-2002 show general decreasing trends in the Atlantic sector of the Southern Ocean, with values at the Antarctic Peninsula declining by roughly 12% (Lee et al., 2010). Chl a concentrations from Elephant Island peaked during 1994-1996, showed low values from 1997-1998 and increased again in 1999-2000 and 2002 (Loeb et al., 2010). In the Antarctic ice algae from multi-year ice can contribute at least 20% to total production, with fast ice showing chl a concentrations up to 120 mg chl a·m−2 (McMinn et al., 2000). It has been noted that phytoplankton is decreasing in the western Antarctic

75 3.5. Discussion

Peninsula (WAP) region and increasing in the southern WAP region due to wind stress and ice free conditions (Montes-Hugo et al., 2009). With ice algae being an important contributor to production, declines in sea ice (and therefore ice algae) are likely to be underestimated by satellite data, or samples only taken in the summer months. The fitting process for krill fails to capture the high biomass in 1982, which could be due to a number of reasons. First, the time series data was only applied to the adult group and not the entirety of all krill functional groups. Looking at the biomass trends over time (appendix P), juvenile krill have a larger biomass and a slightly different trend over time8. Combination of these groups could provide a better fit to data. In addition sampling of zooplankton is highly variable, and could add to variability in data used to fit the model. As long term timeseries are hard to come by, the ones used in the fitting chinstrap, gentoo, and Adelie penguins were obtained from the PALMER LTER research conducted on Anvers Island (Fraser, 2006). While the research indicates delcines of Adelie penguins at Anvers Island are representative of larger scale changes in population, surveys from other breeding locations indicate mixed changes in chinstrap populations. Chinstrap penguin populations have been decreasing since 1981 at King George Island, while at Signy Island populations only started to decline in the 1990s (Woehler et al., 2001; Croxall et al., 2002). For Adelie penguins populations at Signy Island have shown to be stable in the 1970s with fluctuations in the 1980s and 1990s while penguins at Anvers Island have been identified to decline since the 1970s (Woehler et al., 2001; Croxall et al., 2002). In the past it was believed ice-dependent species such as Adelie penguins were decreasing and ice-avoiding species such as chinstrap and gentoo were increasing, but more recent data suggests all species are declining. The paradigm has shifted from the idea that penguin populations were driven by sea ice to

8It should be noted the same timeseries was applied to the juvenile krill group during the ﬁtting process. Only a small portion of juvenile krill would be captured by sampling nets, and therefore be included in the trend. Addition of the krill timeseries to the juvenile krill group did not enhance the model ﬁt.

76 3.5. Discussion one that populations are dependent on krill, which is driving changes in the populations (Trivelpiece et al., 2010). Support for this stems from research highlighting declines in both Adelie and chinstrap populations at the South Shetland Islands up to 75%, with changes in krill biomass potentially ex- plaining these trends (Trivelpiece et al., 2010). Within the model, declines in all penguin species are attributed to changes in krill. While there are no direct linkages to sea ice or other environmental factors in the model, declines of penguin biomass caused by changes in the food web range from 18% for chinstrap to 50% for macaroni penguins. If immigration rates are removed from the model to better represent large scale abundance trends for chinstrap and gentoo penguins, biomass declines a further 24% for chinstrap and 18% for gentoos. Maximum declines of roughly 50% for macaroni penguins are lower than the 75% declines in population noted in Trivelpiece et al. (2010). However, while model results are presented in biomass and other literature in number of breeding pairs, it is important to note that the model supports the theory that changes in krill are responsible for declines in penguin populations. Recent literature indicates immigration rates for chinstrap and gentoo penguins should be removed from the model (Trivelpiece et al., 2010). Both fitted models indicate that changes in primary production and detritus are responsible for declines within the model, implying this is a bottom up ecosystem. The vulnerability for most predator prey interactions was set to the default of 2 indicating a mixed interaction (neither bottom- up or top-down), however changes in higher trophic level biomass are highly influenced by lower trophic level biomass demonstrating strong bottom up influences. The trophic level of the ecosystem remains constant in the face of overall declines in biomass indicating even declines across trophic levels. As the past demonstrates boom and bust cycles of krill and salps (Brierley and Reid, 1999), there is the potential for a ”leveling out” of these species in terms of biomass since the mid 1990s as suggested by Loeb et al. (2009) and Lee et al. (2010). For the constant climate scenario, an increase in most functional groups is observed along with a higher total biomass for the

77 3.5. Discussion ecosystem. As climate drivers are kept constant, this simulation allows the opportunity to assess the potential of stability in lower trophic levels, rather than large annual fluctuations often observed in high latitude ecosystems. While the annual ice and SST patterns are repeated, there are no net changes in model drivers, allowing the changes that do occur in the model to be driven by trophic interactions and harvest. This is supported by Monte Carlo simulations on the Ecopath starting parameters (Figure 3.5), where higher initial biomass of species is supported. Reflecting on the krill surplus hypothesis; the notion that as large baleen whales were harvested throughout the first half of the 20th century, there was a large availability of krill for other whales (minke), seals and penguins (Laws, 1977). While the model does not assess this issue on the same temporal scale, additional, more specific simulations would be required to address this issue fully. However, the model is able to support a higher total biomass, and higher biomasses of predators as shown through Monte Carlo simulations and the constant climate scenario. If commercial whaling reduced baleen whale populations enough to cause large scale increases in seals and penguins, it would have occurred before the start of the model, therefore declines should be considered in the context of recently inflated populations. It is possible that restructuring of some seal, penguin, and whale populations is occurring. Since ice and krill biomass has declined since this time, the ”surplus” caused by whaling would have been short lived in the ecosystem, and would not be representative of present day populations. Krill predators would have had to reach maximum abundance pre 1970s, before ice declines were observed, and inputted Ecopath parameters would reflect changes that already occurred to krill predators in the ecosystem. However without some indication as to which populations are changing we cannot make these assumptions within the model, only to note total biomass declines. The increased harvest scenario shows little change to the biomasses of functional groups or the total ecosystem, while alterations of climate drivers do identify large changes in the model. It is possible that the model is an artifact of the literature used to create it, as much of the research in the Southern Ocean is focused on bottom up approaches (Ainley et al.,

78 3.5. Discussion

2007). While top down approaches to understanding the ecosystem have been employed, generally focusing on management of the ecosystem (Nicol et al., 2007), incorporation into the model does not cause the profound changes that bottom-up forces cause. Harvest of krill at quota levels does not significantly alter ecosystem structure, denoting increase in harvest levels would be appropriate. How- ever, one should take caution in this interpretation as the model only runs on a temporal scale rather than a spatial scale. CCAMLR quotas are set based on ecosystem management, but recent evidence indicates that spatial overlap between krill predators and fisheries is an important link in determining harvest levels. Marin and Delgado (2001) showed that roughly 80% of the krill catch was taken from within penguin foraging areas near the Antarc- tic Peninsula, suggesting fisheries are in direct competition with predators (Hewitt et al., 2002, 2004). The suggested implementation of Small Scale Management Units (SSMUs) will limit spatial harvest by breaking down quotas into smaller spatial scales to reduce competition with land based predators (Hewitt et al., 2004; Flores et al., 2011). In this respect the model is less sensitive to declines in krill, as all functional groups are assumed to be in a homogenous space. However in reality small scale declines in krill, or other prey items can cause declines in breeding success, or starvation as shown in the past (Reid and Arnould, 1996; Brierley and Reid, 1999).

Conclusions

Construction of an ecosystem model and past simulations indicates bottom- up interactions are important at the Antarctic Peninsula. Monte Carlo routines and past simulations at constant climate levels demonstrate the ability of the system to support a higher total biomass. Increasing the krill harvest to quota levels does not result in large changes in the ecosystem, when compared with the impacts of environmental changes. Although more detailed spatial analysis should be considered before management decisions are made.

79 Chapter 4

Future Impacts of Hunting, Fishing, and Climate Change on the Hudson Bay Marine Ecosystem

4.1 Synopsis

Using the ecosystem model constructed in chapter 2, simulations depicting harvest and changes in climate were extended into the future to assess the long term impacts on the Hudson Bay ecosystem. Numerous scenarios corresponding to IPCC climate scenarios were used, with future environmental trends extracted from global climate models and incorporated into the ecosystem model to continue past environmental drivers. In addition, different harvest levels were combined with each possible climate scenario assessing cumulative impacts on the ecosystem. Continuation of environmental drivers resulted in more pronounced shifts in the food web from an ice algae-benthos-benthic fish dominated pathway to a spring bloom- zooplankton-planktivorous fish dominated ecosystem. Bottom up changes in the food web are identified as important factors for determining changes in lower trophic level organisms such as benthos, zooplankton, and fish. Har- vest of higher trophic levels is identified as a more important factor when compared to environmental changes. Simulations indicate some stocks are unable to sustain current harvest levels and may be extirpated (narwhal, eastern Hudson Bay beluga, polar bears, and walrus). Larger populations

80 4.2. Introduction of marine mammals (ringed seals and western Hudson Bay beluga) are iden- tiﬁed to be able to withstand an increase in harvest and continue to increase even under a high climate scenario coupled with an increase in harvest rates. Management and conservation focused on marine mammals should be directed to prevent over-harvest of vulnerable populations, as this is indicated as a more severe threat in the model.

4.2 Introduction

High latitude marine ecosystems are particularly sensitive to climate change (ACIA, 2004) as small changes in temperature can have large effects on the extent and thickness of sea ice (Smetacek and Nicol, 2005), and can fundamentally alter the structure of the food web. The Canadian Arctic is already experiencing a reduction in sea ice thickness and a decrease in sea ice extent (Holland et al., 2006; Arrigo and Pabi, 2008). In addition, while the reliance on hunting has decreased it is not expected to disappear (Csonka and Schweitzer, 2004), especially since human populations are still increasing even though growth rates have slowed since peaking in the 1960s (Bogoyavlenskiy and Siggner, 2004). These stressors (hunting and climate change) have been shown to cause changes to the Hudson Bay ecosystem such as declines in polar bear populations (Stirling et al., 1999, 2004). Here we aim to explore the potential changes to come under increased environmental and hunting pressure, not only to the species they impact, but how these changes will affect the marine ecosystem structure and the cumulative effects of these impacts. Temperatures for Hudson Bay have shown increases ranging from 0.5- 1.5◦C from 1955-2005 (Hansen et al., 2006a), and warmer temperatures have been shown to alter the mean ice freeze-up and break-up dates by 0.8- 1.6 weeks in spring and fall (Hochheim et al., 2010). Also, from 1978 to 1996 declines in sea ice area from 2000±900 km−2y−1 have been observed (Parkinson et al., 1999). Ice plays an important role in the ecosystem, not only as a platform for marine mammals to hunt and breed upon (seals, polar bears) but also as an important regulator of ice algae, a food source

81 4.2. Introduction for lower trophic levels through the winter and into the spring. Algae frozen within the ice are released from brine channels during ice growth and melting (Melnikov, 1998), and exported to the water column. During ice melt, this release combined with low zooplankton biomass (and therefore low grazing rates) in the early spring leads to a higher export to the benthos (Lavoie et al., 2009), this effect is magnified when annual melting of ice is shifted earlier (Hunt et al., 2002). In relation to total production, the contribution of ice algae in southeast Hudson Bay has been estimated at 25% (Legendre et al., 1996), and ranges from 3-57% for other Arctic and sub-Arctic areas (Gosselin et al., 1997). As annual ice levels decline less ice algae is exported to the benthos, potentially decreasing benthic biomass and the fish and invertebrates reliant on these benthic food sources. This is proposed to favor a phytoplankton- zooplankton dominated system over the ice algae-benthos ecosystem typical of the Arctic (Piepenburg, 2005). In addition, lengthening of the growing season and increases in temperature are expected to cause increases to the spring/summer bloom. In the Beaufort Sea, future climate change is anticipated to extend the summer phytoplankton bloom which favors zooplankton development, mainly copepods in the region (Lavoie et al., 2010). This will likely enhance pelagic feeding animals in the food web. Studies on bird diet show shifts from benthic feeding fish to pelagic based species indicating a change in the ecosystem (Gaston et al., 2003). Currently, marine mammals found within the Hudson Bay (HB) region are hunted and consumed through subsistence hunts. However declines in some populations, e.g. eastern HB belugas (Hammill, 2001; DFO, 2002a; Gosselin et al., 2002; Gosselin, 2005; Hammill et al., 2009), northern HB narwhal (COSEWIC, 2004a), and polar bears (Lunn et al., 2002; Stirling and Parkinson, 2006) may jeopardize the ability for hunting to continue at present levels. For marine mammal stocks not demonstrating declines, alterations to the food web caused by changes in climate may ultimately affect population levels, although thresholds have not been identified (see chapter 2). The human population in the Nunavut portion of Hudson Bay has more

82 4.2. Introduction than doubled from 1981-2006, increasing from nearly 4700 to 9500 inhabitants (Statistics Canada, 2006). While growth rates are projected to decline, estimates remain positive indicating continued future growth. Future projections for all communities in Nunavut suggest continued increase for the territory from 32,000 to 45,000 individuals from 2009 to 2036, with growth rates slowing to 1.1% per year toward the end of the projections (Nunavut Bureau of Statistics, 2010). While these predictions assume a decline in human growth rates, many communities are still showing large increases. Compounding the reliance on harvested foods is the belief that the prices of store bought foods will continue to increase despite subsidy programs offered to northern residents (Windeyer, 2011b,a). Food price increases coupled with rising populations may intensify the demands for country foods (foods hunted or gathered from the land). Using an existing food web ecosystem model from 1970-2009 (chapter 2), driven with environmental variables (sea surface temperature (SST) and sea ice) and catches of various species, future simulation emulating different levels of climate change and harvest are utilized to assess the impacts to the food web. While producer groups were driven in the model using SST and sea ice, these showed the greatest responses to environmental changes over time, accounting for changes in the food web. Loss of Arctic sea ice due to increases in temperatures is believed to increase productivity up to three times the 1998-2002 production levels (Arrigo and Pabi, 2008). In addition, less sea ice will increase the availability of light to phytoplankton, a limiting factor of production in some Arctic ecosystems (Conlan et al., 2008). These impacts can account for alterations to the fish communities, through disrup- tion of bentho-pelagic coupling. As sea ice declines, so does the amount of algae exported to the benthos, thus causing declines in benthic and benthic feeding fish species. Declines to polar bears, narwhal, bearded seals, and the eastern Hudson Bay stock of belugas were attributed to hunting pressure. In order to test the Hudson Bay ecosystem model’s sensitivity to further impacts of hunting and climate change, past model environmental and harvest drivers were continued into the future under a variety of hunting and climate change scenarios.

83 4.3. Methods

4.3 Methods

Modeling Approach

For the mass balancing model approach the Ecopath with Ecosim (EwE) software was used, as it utilizes information on trophic interactions to identify changes to the ecosystem (Christensen and Pauly, 1992; Walters et al., 2000; Christensen and Walters, 2004) and provides a common framework between systems studies by different researchers (Plaganyi and Butterworth, 2004). It is currently used in over 154 countries with more than 6000 users, and has been named one of NOAA’s top 10 breakthroughs (NOAA, 2006). While single species models can offer greater details for factors affecting individual species, they fail to capture the linkages important for assessing a food web (Fulton and Smith, 2004). In addition, it would take numerous single species models to identify the potential impacts of the ecosystem that can be addressed with one ecosystem model (Fulton and Smith, 2004).

Model Structure

Future simulations are based upon an existing ecosystem model created for the Hudson Bay marine ecosystem (chapter 2). The existing model was fitted using catch data from 1970-2009 for all harvested species in the region in addition to SST and ice cover as environmental drivers to the ecosystem. In the past fitted model, SST and sea ice were used as environmental drivers with data extracted from the global Hadley Centre Sea Ice and Sea Surface Temperature model (HadISST) from the British Atmospheric Data Centre (BADC, 2010) and used as drivers for primary producer species groups. The geographical region of the model includes James Bay along with Hudson Bay, but excludes Hudson Strait and Foxe Basin (see chapter 2, figure 2.1). The model contains two producer groups; ice algae and pelagic production. The sea ice was inputted as percent ice cover to the region as a driver for the ice algae producer group, while SST was used as a driver for pelagic producer. Ice algae was driven with percentage ice cover rather than ice thickness, due to Chl a biomass in ice cores being found in the bottom 4 cm

84 4.3. Methods

(Juul-Pederson et al., 2008). Furthermore, SST and open water (as calculated by areas not covered by sea ice) have been used in Arctic wide models to predict future production changes (Pabi et al., 2008). The pelagic production group was thus driven with SST to mimic the seasonal production peak in the summer.

Temporal simulations were created using equation 4.1, where dBi/dt represents the change in biomass (B) for group i over the time interval t, with starting biomass Bi. gi ∑represents the net growth eﬃciency (production/consumption ratio),the Qji is the total consumption on group i, ∑ j and Qij is the predation of all predators on group i. MOi represents j the other mortality term (for mortality associated with old age), Fi is the

fishing mortality rate, Ii is the immigration rate, ei is the emigration rate, with the combined term Bi · (ei − Ii) as the net migration rate. Mortality refereed to within the paper entails predation and harvest mortality unless specifically identified. ∑ ∑ dBi/dt = gi Qji − Qij + Ii − (MOi + Fi + ei)Bi (4.1) j j

Using the trophic level of individual species groups (equation 4.2), mean ecosystem and catch trophic levels, TLE and TLC , were calculated for each scenario (using equations 4.3 and 4.4) and presented as the average over the last ten years of each simulation. ∑ TLi = 1 + (Xa ∗ TLa) + (Xb ∗ TLb) + (Xc ∗ TLc)..... (4.2) ∑ Bi TLE = ∗ TLi (4.3) BE ∑ Ci TLC = ∗ TLi (4.4) CE The Trophic level TL of each group was calculated with EwE, where primary producers have a TL of 1, primary consumers with 100% of their diet as producers have a TL of 2, and consumer TL is calculated based on the diets of

85 4.3. Methods other organisms (Christensen et al., 2007). For consumers TL of each group is calculated based on the TL of prey groups (a,b,c,etc.),TLa,TLb,TLc, and the percent contribution of each prey group to the diet, Xa,Xb,Xc. Bi and

Ci are the biomass and catch for group i, and BE and CE are the biomass and catch of the entire ecosystem, with values represented in t · km−2.

Scenarios

Table 4.1: Simulations of varying levels of climate and hunting. Scenario names indicate levels of hunting and climate. First letter indicates either a low (L) or high (H) climate scenario followed by the variance applied to the climate data (either past variance (1) or double the past variance (2)). The second letter indicates the level of hunting applied to the simulation; H1 for constant hunting at the 2009 levels, or H2 for double the hunting of the 2009 levels.

Climate Scenario Hunting Scenario Scenario Abbreviation Low Constant L1H1 Low Double L1H2 Low double variance Constant L2H1 Low double variance Double L2H2 High Constant H1H1 High Double H1H2 High double variance Constant H2H1 High double variance Double H2H2

Future scenarios of climate change were created to identify plausible changes to the ecosystem. Data from the GFDL (Geophysical Fluid Dy- namics Laboratory) CM2.1 coupled climate models (GFDL, 2010) were used as environmental drivers, in keeping with drivers used to ﬁt the past model (chapter 2). Two main scenarios depicting a future ”Low” and ”High” climate scenario were created by extracting regional sea ice (percent cover) and SST data from the global model. The ”Low” climate scenario corresponds to the IPCC (Intergovernmental Panel on Climate Change) constant 2000 scenario, while the ”High” scenario corresponds to the A1B scenario9.

9 The constant 2000 scenario assumes constant CO2 emission equivalent to emissions for the year 2000. The A1B scenario, while considered a moderate emissions scenario, demon-

86 4.3. Methods

Each model simulation combined past and future datasets to provide a continuous 100 year time series. The past, re-created model (chapter 2) was combined with future climate drivers and harvest to assess the future impacts, with drivers being combined into continuous time series. For each future climate scenario (Low and High) spanning 2009-2069, 100 datasets were generated from the IPCC future climate models with difference variance levels, to test the model sensitivity to different climate and variance levels. Although no significant trends in variance were identified within the past data (1970-2009), we utilize different levels of variance for future data to test the impacts. Multivariate covariance with a normal distribution derived from the 1970-2009 environmental data (ice and SST) was applied to each of the future scenario datasets to generate 100 different time series datasets to force the Ecosim model. Each generated time series used the future data as the mean for the trend, and applied covariance from the past environmental data to generate a unique time series dataset to be used as a model driver. Next, a second set of environmental data was generated with a doubling of the variance in order to account for larger fluctuations in future climate variables. For climate scenarios L and H reflect a low or high climate scenario, while numbers 1 or 2 indicate whether normal or double variance was applied. Each dataset containing 100 simulations was used to drive the ecosystem model, with results recorded as the biomass of each functional group over the last 10 years of the simulation. Environmental drivers were extracted from the GDFL CM2.1 coupled model. Figure 4.1 identifies changes in ice cover and SST from the starting of the model in 1970 (using the HadISST model data for past data). The Low and High future climate scenarios demonstrate a lengthening of the ice free season, as well as increased temperatures and lengthening of warmer water periods (above 0◦C).

strated higher sea surface temperature and lower ice cover than more extreme emissions scenarios such as the A2 scenario. Since the data displayed more extreme changes, it was chosen as more representative of a high climate change scenario

87 4.3. Methods

A B C °

Past Past Days below 0 below Days Low Low High High Days below 25% Ice cover below Days 50 100 150 200 250 300 50 100 150 200 250 300

1980 2000 2020 2040 2060 1980 2000 2020 2040 2060 Year Year

C 1970−1975 1970−1975 D 2064−2069 Low 2064−2069 Low 2064−2069 High 2064−2069 High C) ° SST ( Ice Cover (%) Ice Cover −2 0 2 4 6 8 0 20 40 60 80 100

2 4 6 8 10 12 2 4 6 8 10 12 Month Month

Figure 4.1: Environmental data used in model simulations. Past values were obtained from the HadISST data set while future data were extracted from the GDFL CM 2.1 coupled model. A and B compare number of days below 25% ice cover or days below 0◦C for SST from the past with low and high climate scenario data. C and D show the ice cover and SST trends for the ﬁrst 5 years of the model (1970-1975) compared with ending values (2064-2069) for the low and high climate scenarios.

88 Table 4.2: Summary of harvest values and hunting/fishing mortalities used for the initial Ecopath model (1970), and future hunting scenarios: H1 where catch and effort are constant to 2009 values, and H2 where catches and effort are doubled from the 2009 values.

Species Group M (y−1) Catch (#) Reference 1970 2009 1970 H1 H2 WHB Polar Bears 0.033 0.078 44 47 94 Lee and Taylor (1994); Aars et al. (2005) SHB Polar Bears 0.058 0.089 68 25 50 Lee and Taylor (1994); Aars et al. (2005) FB Polar Bears ∗ 0.024 0.026 142 106 212 Lee and Taylor (1994); Aars et al. (2005) Killer Whales 0.051 0.040 0.25 0.738 1.477 Higdon (2007)(Ferguson pers. comm.) Narwhal 0.008 0.072 23 82 164 DFO (1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998); Stewart and Lockhart (2005); JCNB/NAMMCO (2009) Bowhead 0.003 0.001 0.25 0.37 0.74 Higdon (2008)(Ferguson pers. comm.) Walrus N 0.031 0.012 74 38 76 Strong (1989); NAMMCO (2005b); Stewart and Lockhart (2005) Walrus S 0.009 0.019 8 14 28 Strong (1989); NAMMCO (2005b); Stewart and Lockhart (2005) Beluga E 0.032 0.035 83 47 94 de March and Postma (2003); JCNB/NAMMCO (2009) Beluga W 0.005 0.002 152 106 212 de March and Postma (2003); JCNB/NAMMCO (2009) Beluga James 0.019 0.009 35 34 68 de March and Postma (2003); JCNB/NAMMCO (2009) Bearded Seal 0.045 0.164 556 1187 2374 Stewart and Lockhart (2005); Statistics Canada (2006) Harbour Seal 0.002 0.007 27 151 302 Stewart and Lockhart (2005); Statistics Canada (2006) Ringed Seal 0.008 0.030 8436 45215 90430 Stewart and Lockhart (2005); Statistics Canada (2006) Harp seal 0.014 0.051 91 576 1152 Stewart and Lockhart (2005); Statistics Canada (2006) Birds 0.005 0.023 213703 1299831 2599662 Stewart and Lockhart (2005); Statistics Canada (2006)

Continued on Next Page 89 Table 4.2 Continued

Species Group Fishing Mortality y−1 Catch (tonnes) Reference 1970 2009 1970 H1 H2 Arctic Char 0.0011 0.0051 421.4 2192.7 4385.4 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Atlantic Salmon 0.0002 0.0008 24.08 135.5 271.0 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Gadiformes 0.0003 0.0014 240.8 596.2 1192.5 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Sculpins/ Zoarcids 0.0007 0.0032 240.8 668.2 1336.4 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Capelin 0.0003 0.0012 120.4 884.1 1768.2 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Sandlance 0.0001 0.0004 60.2 507.0 1013.9 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Other Marine Fish 0.0002 0.0008 60.2 413.8 827.5 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) Brackish Fish 0.0005 0.0022 24.08 132.7 265.5 Stewart and Lockhart (2005); Booth and Watts (2007); Statis- tics Canada (2006) ∗ Indicates hunting mortality is calculated based on a percentage of catches taking place within the model area. 90 4.4. Results

Under each of the various climate scenarios two hunting levels were tested: ”H1”: where catches and effort are kept constant to current (2009) levels, and ”H2”: where catches and effort are doubled from the 2009 values to reflect increases in human populations and the potential desire for higher catches (table 4.2). A summary of all model simulation combinations are provided in table 4.1. Harvest data was provided from a variety of sources. For species hunted under regulations or quotas (polar bears, narwhal, belugas, walrus) recorded data was available for most years, or averages over time spans (generally 5 years) indicating averaged harvest rates. For unregulated species (birds, seals, and fish) catches were determined based on per capita rates from past harvest studies (Stewart and Lockhart, 2005), and driven with changes in human population levels based on past census data 10 (Statistics Canada, 2006). 100 simulations were run for each scenario. The average biomass (t/cdotkm−2) over the last 10 years of the simulation was used to get the mean and 95% CI for biomass changes of each functional group at the end of the simulation. This is mostly important for lower trophic level groups where the model is driven, and variations in the annual cycle of environmental drivers may cause changes in biomass.

4.4 Results

General Results

With the reductions in ice algae driven by sea ice there is a continued decrease in the availability of ice detritus to benthos. While this has already been identiﬁed through a past simulation from 1970-2009 (see chapter 2), longer simulations enhance this decline. Conversely, increased temperature favored the pelagic production to zooplankton pathway, causing a more pronounced shift from a benthic to a pelagic ecosystem (ﬁgure 4.2).

10It should be noted that for each functional group either catches or eﬀort was applied, not both.

91 > 75.1%

Polar Bear Foxe PolarSH Bear Polar Bear WHB sealHarp Seal Ringed Narwhal Sharks/Rays Harbour Seal Beluga W Beluga James Bearded Seal Seabirds Beluga E Cephalopods Walrus S Salmon Atlantic Bowhead Walrus N Arctic Char Gadiformes Brackish Fish Sculpins/Zoarcids Capelin Sandlance Other Marine Fish Euphausids MacroZooplankton Echinoderms Crustaceans Other MesoZoopl. Marine Worms Bivalves Other Benthos Copepods MicroZoopl. Primary Production Ice Algae Ice Detritus Detritus Pelagic 50.1% to 75% L1H1 25.1% to 50% L1H2 0.1% to 25% L2H1 -25% to 0% L2H2 -25.1% to -50% H1H1 H1H2 -50.1% to - 75% H2H1 < -75.1% H2H2

Figure 4.2: Changes in biomass for each scenario. Mean values of the 100 simulations are presented as the percent change from the starting 1970 biomass. Mean and 95% CI ranges of biomass for all simulations are presented in Figures 4.9,4.10, 4.11, and 4.12. Killer whales were excluded from this ﬁgure as the biomass for this group was forced. 92 4.4. Results

Larger CI are observed for both biomass and mortality under scenarios with a doubling of environmental driver variance (figures 4.9, 4.10, 4.11 and 4.12), however mean values are not statistically different. This suggest the model is more sensitive to the general trend in climate drivers. Doubling harvest catch/effort identifies many species that can withstand increased harvest levels; most fish groups, harp seals, ringed seals, harbour seals, and beluga (western and James Bay stocks). Narwhal and walrus (north and south) were identified to have relatively stable changes in biomass for past simulations, however continued hunting at current rates is not sustainable. Polar bears have shown declines in the past model, and under continued constant hunting pressure these populations are extirpated. Trophic level of the ecosystem continues to remain relatively stable for all scenarios with only small changes from the past (1970 values), low climate scenario, and high climate scenario. The trophic level of catch (TLC ) changes between scenarios as catch or effort was doubled, which resulted in similar proportions of each species being harvested, thus not greatly changing the composition of TL of catches (table 4.3). For 1970 the TLC was 3.57. Under the low hunting (H1) scenarios this value increases to 3.62- 3.65 reflecting small increases in catches of higher trophic level organisms.

Under the high hunting scenarios (H2) the TLC decreases slightly to 3.59- 3.64. While it is important to note catches and eﬀort were forced within the model, the slightly lower value under the high hunting scenarios is a result of declines in some populations (walrus, eastern beluga, and narwhal).

Trophic level of the ecosystem TLE decreases from the 1970 value of 2.15 to a value of 2.11 for the low climate scenarios (L1 and L2), and starts to increase under the high climate scenarios (H1 and H2) to 2.13. While there is a loss of some higher trophic level predators (polar bears, narwhal, eastern HB beluga, and walrus), growth in other populations compensates for these losses with the other high trophic level animals (killer whales, western HB belugas, James Bay belugas, ringed seals, harp seals, and harbour seal). Total ecosystem biomass is higher under the future high climate scenario when compared to the 1970 value (table 4.3) indicating that although there is a loss of some species, the ecosystem is overall able to withstand a higher

93 4.4. Results total biomass. Simulation results for key species are presented comparing the L1H1 scenario and the H1H2 scenario. As variance does not signiﬁcantly change the mean biomass or mortality we examine the changes caused to these groups between these two most extreme scenarios.

Table 4.3: Trophic level of ecosystem (TLE) and catches(TLC ) for the Eco- path model (1970) and each simulation. Results presented are averages values for the last 10 years of each simulation. Total biomass and total catch are presented in t · km−2 for all species within the ecosystem.

TLE TLC Total Biomass Total Catch 1970 2.155 3.578 58.305 0.002 L1H1 2.114 3.626 64.185 0.009 L1H2 2.119 3.594 63.511 0.018 L2H1 2.114 3.626 63.066 0.009 L2H2 2.118 3.593 63.095 0.018 H1H1 2.133 3.654 70.936 0.011 H1H2 2.135 3.638 70.640 0.020 H2H1 2.132 3.655 70.363 0.011 H2H2 2.134 3.638 70.801 0.020

Producers

Primary producers were directly affected by environmental drivers, as SST and ice cover were used as multipliers of pelagic production and ice algae, respectively. General trends show an average increase in biomass of 56% for pelagic production for the L1 (Low climate scenarios), and an average increase of 105% for the H1 (High climate scenarios) based on the 1970 value (figure 4.9). For ice algae the L1 scenario caused an average decrease of 31% and the H1 scenario caused a decrease of 53%. Increased flow from pelagic production to pelagic detritus resulted in an increase in pelagic detritus of 44% and 86%. The declines in ice algae result in decreases in ice detritus of 22% and 39% for the L1 and H1 scenarios respectively. Biomass trends by scenario indicate mean biomass remains constant for the low and high climate scenarios, yet scenarios with high variance in model

94 4.4. Results drivers (L2 and H2), result in a larger variance in producer biomass at the end of the model simulation (ﬁgure 4.10). The composition of annual primary production was 70% pelagic production and 30% ice algae at the start of the model (1970). At the end of the Low future scenario pelagic production represented 84% of the annual primary production and ice algae represented 16% of annual production. Under the High climate scenario, pelagic production and ice algae contribute 91% and 9% to the annual production respectively. The total production (ice algae and pelagic production combined) increases by 15% and 18% for the Low and High climate scenarios.

Benthos

Benthic groups in the model decline due to decreased ice detritus from ice algae. The exception is the functional group crustaceans which includes benthic and pelagic crustaceans (Amphipoda, Cirripedia, Cumacea, Decapoda, Isopoda, Nebaliacea, Ostracoda, Pycnogonida, and Tanaidacea). For the other benthic groups, decreases are identified to range from 10% for bivalves to 26% for the other benthos group under the low climate scenarios, while these declines increase to 14% for bivalves and 42% for the other benthos group under the high climate scenarios. Benthic groups follow the same patterns as producers for ending biomass, i.e., low and high climate scenarios dictate the mean biomass, while higher variance in the environmental drivers is important in determining the variance in biomass results (figure 4.10). It is also important to note that while mean biomass of benthic groups is decreasing from the low to high climate scenarios, mortality rates are showing slight declines (figure 4.12) indicating changes in these groups are not caused by predators, but rather by bottom up forcing.

Zooplankton

While benthic groups generally mimic biomass and mortality patterns for ice algae, zooplankton follow the responses of pelagic production. Mean biomass increases from the low to high climate scenarios, with patterns in variance

95 4.4. Results replicating the patterns from the pelagic production group for each scenario (ﬁgure 4.10). Increases range from 18% for both the euphausiids and micro- zooplankton groups to 29% for the macro-zooplankton group under the low scenarios to 40% for euphausiids and 69% for macro-zooplankton under the high scenarios. Mortality rates do increase for zooplankton groups indicating higher predation through the food web. Increases in mortality are caused by increases in predators along with predators consuming more zooplankton to compensate for declines in benthic populations. As biomass increases with mortality rates (ﬁgure 4.12), it appears zooplankton are able to sustain higher predation levels, as the bottom up changes in the food web are able to support higher biomasses and thus the increased predation.

Fish

The fish trophic level of the ecosystem is where we start to identify the impacts of harvest in addition to prey changes. Fish groups are harder to distinguish as they are impacted directly through fishing, bottom up changes, and changes in predator population. In order to tease out some of these individual impacts, sandlance, capelin, and gadiformes are displayed in further details (figures 4.9 and 4.10).

Sandlance

Comparing the L1H1 and H1H2 scenarios for sandlance illustrates the increases in prey biomass between scenarios for the four highest ranked prey groups (copepods, euphausiids, other meso-zooplankton, and micro-zooplankton). Ringed seals had the largest contribution to total mortality as predation from this group alone for the initial 1970 value was 0.343y−1 out of a total mortality of 0.849y−1 (ﬁgure 4.3). Yet, as ringed seals increase in biomass from 29% to 64% for the L1H1 and H1H2 scenarios respectively, mortality caused by ringed seals only increases by 7% and 11% indicating the sandlance population is able to increase enough to meet predator’s demands. Increases in hunting show catches increasing 6 to 20 times the starting values for the low and high hunting scenarios, with ﬁshing mortality reaching

96 4.4. Results nearly 8 times the initial value. However total mortality (predation and fishing combined) is still lower than the 1970 value, even for the H1H2 scenario. While these values appear to be extreme, it should be noted that initial model values for fishing represented a very small contribution to total mortality, so even if these increases seem large, the fishing mortality for the H1H2 scenario was only roughly 1% of total mortality.

Capelin

Results for capelin are very similar to sandlance. The top four contributors to diet (copepods, euphausiids, macro-zooplankton and pelagic producers) all increase under the low and high climate scenarios. For the L1H1 scenario euphausiids have the smallest increase in biomass at 18%, while pelagic producers increase 57% (ﬁgure4.4). For the high climate scenario euphausiids increase 40% and pelagic producers 105%. Predation by ringed seals and seabirds in addition to catches all increase from the L1H1 scenario to the H1H2 scenario, although total mortality is lower under both scenarios indicating capelin population are able to increase enough to meet these demands as capelin biomass more than doubles under the H1H2 scenario.

Gadiformes

The gadiform group representing Arctic and polar cods has a diet more heavily reliant on epibenthic prey items. Decreases in biomass of prey items forming the greatest contribution to the diets range from a 3% decline for echinoderms to a 27% decline for the other benthos group under the L1H1 scenario. Under the H1H2 scenario biomass declines of prey items continue ranging from 14% for bivalves to 42% for the other benthos group (Fig 4.5). Total mortality increases under both scenarios to a maximum of 0.549y−1 in the H1H2 scenario compared to the 1970 value of 0.477y−1. Although catches and ﬁshing mortality have increased from past values, even under the High harvest scenario (H1H2) ﬁshing mortality accounts for less than 1% of total mortality. Nearly all mortality is caused by predation, further supported by the increase in biomass of gadiformes under the high hunting

97 4.4. Results scenarios. When predators are harvested in higher quantities, less predation prevents such large declines. This coupled with decreases in prey biomass identiﬁes this group as declining due to both bottom up and top down forces.

98 Sandlance L1H1 Sandlance H1H2 +29% +64% Ringed F+784% Seal M+7% Catches Ringed Catches Seal M+11% F+340% +76% +144% +676% +2056%

Sandlance Sandlance

Copepods Other Meso- Copepods Zooplankton Other Meso- Zooplankton +28% +19% Micro- +60% Micro- Euphausiids Zooplankton +44% Euphausiids Zooplankton

+18% +18% +42% +40% (a) S=0.849, E=0.731 (b) S=0.849, E=0.702

Figure 4.3: Changes in biomass for sandlance with important contributors to diet and mortality. S (mortality at the start of the model 1970) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open

99 circles represent initial biomass for the Ecopath model (1970 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass. Capelin H1H2 +7% Capelin L1H1 +35%

Seabirds Seabirds Catches +29% +64% F+804% M+6.5% Catches M+15% Ringed M NC +1810% Seal F+354% Ringed +628% +115% +60% Seal M+1%

Capelin Capelin

Copepods Copepods Macro- Macro- Zooplankton Zooplankton +28% +60 Pelagic +30% +69% Pelagic Producers Euphausiids Producers Euphausiids

+57% +18% +40% +105%

(a) S=1.688, E=1.527 (b) S=1.688, E=1.522

Figure 4.4: Changes in biomass for capelin with important contributors to diet and mortality. S (mortality at the start of the model 1970) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open circles represent

100 initial biomass for the Ecopath model (1970 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass. Gadiformes L1H1 Gadiformes H1H2 +29% +64% +6% +40% +32% +75% Ringed Seal Ringed All Beluga All Beluga Seal Catches Catches M+3% M+42% M+24% M+33% F+488% F+950%

Gadiformes Gadiformes

-76% -83%

Worms Worms Bivalves Bivalves -14% -22% -10% -14% Other Other Echinoderms Benthos Benthos -27% Echinoderms -3% -29% -42%

(a) S=0.477, E=0.537 (b) S=0.477, E=0.549

Figure 4.5: Changes in biomass for gadiformes with important contributors to diet and mortality. S (mortality at the start of the model 1970) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1970 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass. 101 4.4. Results

Other Fish

The biomass of the shark/ray group was very small in the initial 1970 model with the only predator being killer whales (0.5% of the total diet) with no fishing mortality. However as killer whales increased, the increased predation mortality was great enough to cause declines in the shark/ray model group. Biomass increased for the brackish fish group, Atlantic salmon, and Arctic char through bottom up changes as the diets contain large proportions of zooplankton or zooplankton feeding fish. While there are benthic components to some diets, they are small in comparison to the plankton contribution, and these groups have the ability to compensate for loss of benthic prey sources with plankton within the model. Mortality for these groups either declines or remains stable (figures 4.11 and 4.12) in all future scenarios.

Marine Mammals

Northern Walrus

Northern walrus show small declines under the low hunting scenarios, but cannot withstand a doubling of harvest levels (figure 4.2). However, there is also a bottom up impact from climate change indicating walrus are sensitive to both top down and bottom up changes. Under the L1H1 scenario the biomass decreases by 11% with a small increase in mortality from 0.173y−1 to 0.186y−1. There are slight to moderate decreases in the biomass of prey groups ranging from 3% for echinoderms to 27% for other benthos (figure 4.6). Under the high climate scenario, H1H2, these declines increase to a minimum of 14% for bivalves to 42% for other benthos. When comparing all scenarios for northern walrus (figure 4.2), changes in climate do appear to be responsible for some of the declines along with harvesting. Biomass decreases by 51% for the H1H1 and H2H1 scenarios (high climate with low and high variance coupled with constant harvest rates), which is higher than the low climate scenarios with constant harvest rates of 11% indicating that there is still a substantial decline in this population due to bottom

102 4.4. Results up forces (roughly 40%). However under the high hunting scenario, H1H2 ending mortality increases to 3.170y−1, and with all high hunting scenarios the biomass is reduced by 99-100%. As killer whale biomass was forced to emulate increased sightings in both scenario (thereby having identical results for killer whales), the cause of the increased mortality is due to a doubling of harvest levels. Although population levels increased by 26% from 1970-2009 (chapter 2) this population is not able to withstand current harvest rates, and a doubling of harvest is detrimental to the population.

Ringed Seals

The ringed seal group is shown to benefit under all scenarios. When comparing the L1H1 and L1H2 scenarios the biomass increases from 29% to 64% with total mortality remaining relatively constant even under higher harvest (figure 4.7). The ringed seal’s most significant predators are polar bears. However under all scenarios all polar bear groups decline nearly 100% removing this group as a source of mortality 11. Therefore removal of ringed seals through increased harvest was not enough to suppress populations at the same level caused by polar bears. Compensation for declines in sculpins/zoarcids and gadiformes in the diet was provided by increases in sandlance and capelin groups.

Narwhal

Recreation of the past HB ecosystem identiﬁed past slight declines in narwhal (14% decline from 1970-2009). However, current results for future simulations indicate narwhal cannot withstand increases in mortality from the starting value of 0.088y−1 to 0.185y−1 for the L1H1 scenario and 3.016y−1 for the H1H2 scenario. Similar to ringed seals, the largest contributors to the diet show changes with decreases in sculpins/zoarcids and gadiformes, with increases in capelin and sandlance. Yet, whereas ringed seal biomass increases, narwhal biomass declines by 59% for the L1H1 scenario and 97%

11Foxe Basin polar bear catches were simulated as relative catches rather than forced due to only a small population being located within the area.

103 4.4. Results for the H1H2 scenario due to the high mortality. It should be noted there appears to be a rebounding of the population under the H1H1 and H2H1 scenarios, due to increased predation of ringed seals by killer whales. As the ringed seal population increases, they make a larger contribution to the diets of killer whales, thus decreasing the predation on narwhal. The predation mortality remains at an increased level of 48% (ﬁgure 4.8) indicating that as the population decreases, predation mortality caused by killer whales does not increase further.

104 N Walrus L1H1 Killer Whale N Walrus H1H2 +284% Killer Whale +284% Catches Catches*

-100% M+354% -48%

F-42%

N. Walrus N. Walrus* -11% -100%

Other Other Benthos Benthos Echinoderms -27% Echinoderms -29% Marine -42% -3% Worms Marine Bivalves Worms Bivalves -14% -10% -22% -14%

(a) S=0.173, E=0.186 (b) S=0.173, E=3.170

Figure 4.6: Changes in biomass for northern walrus with important contributors to diet and mortality. S (mortality at the start of the model 1970) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1970 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass. *Indicates ending biomass is reduced by 100% 105 Ringed Seal H1H2 Ringed Seal L1H1

Killer Whale Polar Catches +284% Polar Catches Bears * +1229% Killer Whale Bears * +480% -100% +284% -100% M-100% M-100% +64% M+296% +29% M+296% F+348% F+765% Ringed Ringed Seal Seal

Sculpins/ Sculpins/ Zoarcids Zoarcids Gadiformes Gadiformes -84% -64% -83% -76% Sandlance Sandlance Capelin Capelin

+143% +76% +111% +60%

(a) S=0.159, E=0.154 (b) S=0.159, E=0.165

Figure 4.7: Changes in biomass for ringed seals with important contributors to diet and mortality. S (mortality at the start of the model 1970) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1970 value), while the shaded circles represent the ending 106 biomass and are scaled to represent the percent change in biomass. *Indicates ending biomass is reduced by 100% Narwhal H1H2 Narwhal L1H1 Killer Whale Catches Killer Whale +284% +378% +284% Catches +252%

M+48% M+48% F+16679% F +754% Narwhal Narwhal -97% -59%

Sculpins/ Sculpins/ Zoarcids Zoarcids Gadiformes Gadiformes -84% -64% -83% -76% Other Marine Other Marine Fish Capelin Fish Capelin +94% +57% +111% +60%

(a) S=0.088, E=0.185 (b) S=0.088, E=3.014

Figure 4.8: Changes in biomass for narwhals with important contributors to diet and mortality. S (mortality at the start of the model 1970) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1970 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass. 107 4.4. Results

Other Marine Mammals and Birds

Current harvest levels of some marine mammals cannot be sustained until the end of the model simulation causing the biomass of these groups to decline to or near a biomass of 0t · km−2. Declines of all polar bear groups was due to high mortality rates caused by harvest (figure 4.11) as this is their only source of mortality within the model. Eastern Hudson Bay beluga also follow this pattern, where constant continued harvest at the current level cannot be sustained. Starting mortality for this group was 0.069y−1 and increases to roughly 3y−1 by the end of the high hunting scenarios. The southern walrus ending mortality is very high (3-3.5y−1) compared to the 1970 value of 0.097y−1. Past simulations indicate a decline of 10%, but the level of harvest cannot be maintained until the end of the model simulation. Harbour and harp seals follow trends similar to ringed seals. With an increase in prey, and declines in some predators (polar bear populations) the populations grow. While there is harvest of these groups, the mortality is not enough to suppress these populations. Bearded seals do not follow this pattern in the model as they are unable to withstand the increase in mortality. Diet of bearded seals includes a larger component of benthic prey items indicating there may be some bottom up driven changes to the population. Mortality increases from the 1970 value of 0.176y−1 to roughly 0.23y−1 and 0.43y−1 under the low and high harvest scenarios respectively (figure 4.9). Bearded seals already showed declines from 1970-2009, thus indicating previous hunting mortality was high. Killer whales were forced within in the model, as sightings have increased, but at a rate much higher than captured by the past model. Therefore the population was kept constant at the 2009 level (with large increases in the past, see appendix A) for future simulations. While prey for the bowhead population increases (zooplankton), mortality rates remaining stable throughout all scenarios. As harvest for this group is low, there is potential for the stock to increase under all scenarios. Since the mortality rates are not significantly affected by increases in harvest, changes to bowheads are being caused by increases in prey avail-

108 4.5. Discussion ability. This is also the case for James Bay and western HB belugas. Both beluga groups increased during 1970-2009 with a continued trend into the future simulations. These groups diﬀer from the eastern HB beluga in that the starting hunting mortality is much lower, and these two populations are large enough to withstand continued harvest rates within the model. The seabird group increases range from 8% to 15% for the low climate scenarios and from 34% to 40% for the high climate scenario. Mortality increases slightly under all scenarios, but as this model group has a diverse diet, there is the ability to compensate for loss of benthic food sources to more pelagic based ones.

4.5 Discussion

Climate forcing in the model is taken from coupled models incorporating atmosphere, ocean, and sea ice models to make predictions based on climate change scenarios (GFDL, 2010). The ”Low climate” scenario in this model should be considered conservative, as under this scenario greenhouse gas emissions are assumed to remain constant at 2000 emission levels, something which has already been surpassed by present day greenhouse gas levels. Even the high climate scenario depicted by the IPCC scenario A1B could be considered moderate, as ice still reaches a maximum ice cover albeit for much shorter time periods than other scenarios. For Hudson Bay it is believed that sea ice will disappear with a doubling of CO2 (Gough and Wolfe, 2001), an occurrence for all future states predicted by the IPCC (IPCC, 2007), with the exception of the constant 2000 emission scenario. It should be noted that these models may underestimate the impacts of green house gas loading, meaning that decreases to sea ice may occur faster than modeled (Stroeve et al., 2007). Some models predict the Arctic will undergo near ice-free summers by the year 2040 (Holland et al., 2006) or between 2050-2100 (Stroeve et al., 2007). Previous IPCC models failed to capture the extreme ice minimum of 2007, as unexpected and large scale ﬂuctuations in climate are possible (ACIA, 2004). Preliminary evidence from the IPCC Fifth Assessment suggests predictions of temperature increases will be more

109 4.5. Discussion extreme than predicted by climate models used in this study (Glikson, 2011; Mah, 2011). Although drivers (SST and sea ice cover) have been shown to cause expected changes to Hudson Bay for past simulations, their ability to represent all aspects of climate change is limited. Factors such as snow cover, solar radiation, freshwater inputs, stratiﬁcation, and ocean circulation all play a role in determining types and amounts of production (Stewart and Barber, 2010; Taucher and Oschlies, 2010). However the linkages of these factors to species within the Hudson Bay ecosystem are limited. They have been excluded not as oversight, but rather due to lack of understanding to the large scale implications to the system. As more studies are completed in the future, additional information can be incorporated into the model to capture a wider array of environmental variable and their impacts on production.

Producers

Changes to the composition of producers occur under all scenarios with pelagic production increasing and ice algae decreasing. While changes to individual producers may appear extreme, total production increase is more modest ranging from 15% to 18% from the 1970 values accounting for both producer groups. Studies from 1998-2003 for Arctic primary production show increases of 30% attributed to a tripling of CO2 (Pabi et al., 2008), while future projections indicate a further increase of 20 to 30% in high latitudes (Richardson, 2008), indicating model estimates may be low as they also include future changes. For the Atlantic Ocean future changes in CO2 are predicted to increase production by 15 to 19%, although when accounting for other climate changes the overall increase is likely to be lower (Hein and Sand-Jensen, 1997). Hudson Bay is not as productive as high Arctic areas but the changes to primary producers suggested by the model appear to be on par with reported and predicted values for other high latitude areas.

110 4.5. Discussion

Benthos

Declines in benthic biomass are attributed to the reduction in ice algae detritus within the model. This implies that benthos are reliant on detrital matter sinking during the spring melt of sea ice. Stable isotope studies in Norway identify ice algae as the main food item for benthos, and changes in climate reducing their preferred food source could alter distribution and abundance of benthos with far reaching ramifications to higher level predators (McMahon et al., 2006). In productive Arctic areas chlorophyll a (Chl a) is significantly correlated to benthic biomass (Chukchi Sea), with declines in carbon flux to benthos causing declines in standing stock biomass of 50% from 1998-2004 (Bering Sea) (Dunton et al., 2005; Grebmeier et al., 2006). However, in less productive marine regions, such as the Beaufort Sea, this relationship between Chl a and benthic biomass is not as strong as in high productive areas (Dunton et al., 2005). The link between ice algae and benthic biomass has not been identified for Hudson Bay as there are no comprehensive studies to show changes in ice algal biomass (or even standing stock of total production for the whole region). However, sea ice algae are a major component of biomass in first year Arctic sea ice (Riedel et al., 2006). Investigation of benthos in Arctic glacial bays reveals climate warming will lead to declines in biodiversity (Wlodarska-Kowalczuk and Weslawski, 2001). While the details within this model are not precise enough to model finer resolution changes, the fact that certain species groups fare better than others indicates there will be some changes to benthic composition and most likely diversity as well.

Zooplankton

Increases in zooplankton groups in the model were driven by increases in pelagic production which peaks between June and September depending on the model scenario (ﬁgure 4.1). In the northwest Atlantic, freshening of the ecosystem is believed to cause greater phytoplankton production and a reorganization of zooplankton favoring smaller shelf associated copepods (Pershing et al., 2005; Greene and Pershing, 2007). While diﬀerent zoo-

111 4.5. Discussion plankton species are predicted to react differently, changes in climate are expected to cause general changes in distribution, assemblages, abundance, timing of life history events, and spatial match-mismatch with predators (Gremillet et al., 2008; Richardson, 2008). Trophic mis-match between timing of zooplankton and phytoplankton blooms was not accounted for within the model, although timing of some zooplankton blooms are driven by temperature, whereas phytoplankton blooms are driven primarily by light (see Richardson, 2008, for a summary of zooplankton studies). Temporal scale of annual blooms for phytoplankton or zooplankton is not tuned to species specific information, and therefore does not account for the effects of changes in the spring bloom-zooplankton peaks and the consequences to zooplankton populations. As zooplankton groups are aggregated in the model, more information will be necessary to expand the model groups for a greater understanding of specific species. In- clusion of region specific information on zooplankton responses to climate change would likely suggest which species will succeed and which will decline. This might also entail a restructuring of the functional groups within the model to highlight key indicators to climate change. However, as only two zooplankton surveys have been completed to date within Hudson Bay (Harvey et al., 2001, 2006) information of this quality is not likely to be available in the near future.

Fish

Fishing mortality contributes a small percentage to total fish mortality (less than 1% contribution to total mortality in most cases) indicating bottom up changes in the food web (i.e., changes in benthos and zooplankton populations) have a greater effects over biomass than harvest rates. While initial fish catches were estimated based on per capita rates (Booth and Watts, 2007, and chapter 2), and increased with human population growth, it is possible they are underestimated due to under reporting or low initial per capita estimates. However when harvest rates were doubled in the model simulations some species were able to continue to increase. In general, Arc-

112 4.5. Discussion tic herring and cod fisheries are predicted become more productive under climate change, with declines to freshwater fisheries (ACIA, 2004). Past fisheries attempts have not shown to be profitable in Hudson Bay (Stewart and Lockhart, 2005) due to accessibility and costs associated with fishing in this region. If these hindrances decline in the future, model results identify fishing effort should be focused on species predicted to increase in the future such as capelin, sandlance, and to a lesser quantities, Arctic charr. These species all display low harvest mortality compared to total mortality indicating there is the potential to increase harvest beyond the High harvest scenario levels without compromising the future biomass levels.

Marine Mammals

Other studies have assessed the future impacts of climate change on marine mammals, sometimes with conflicting results (Burek et al., 2008; Ferguson et al., 2005; Laidre et al., 2008; Moore and Huntington, 2008; Huntington, 2009). Polar bears and narwhal appear to be the most sensitive of all Arctic marine mammals to climate change due to specialized feeding, dependence on sea ice, and small sub-populations, while ringed seals and bearded seals have large circumpolar populations making them considered less sensitive (Laidre et al., 2008). Ice breeding pinnipeds (harp, ringed, bearded seals and walrus) will likely experience declines due to ice melt and retreat of ice shelves, unless they adapt to breed on land (Moore and Huntington, 2008). Polar bears are the only group to include an ice-mediation function within the model (appendix A), specifying that prey becomes less vulnerable as sea ice declines. Although changes in polar bear foraging ability affecting fitness have been well documented (Stirling and Derocher, 1993; Lunn et al., 2002; Stirling, 2002; Stirling and Parkinson, 2006), the importance of hunting mortality in the model far surpasses mortality caused by changes in the food web. The model simulations highlight the high harvest rates of other marine mammal stocks as well (narwhal, eastern HB beluga, walrus). As future simulations cannot be verified, research into sustainable harvest levels for these species in particular would be useful in preventing over harvest.

113 4.5. Discussion

It is anticipated that nutritional stress will become an issue as marine mammal and bird diets shift away from Arctic cod to less energetically rich species (Tynan and DeMaster, 1997; Gaston et al., 2005; Burek et al., 2008). However in the model, diets of many piscivorous predators shift from Arctic cod, polar cod, sculpins and zoarcids to capelin and sandlance. Energetic values of ﬁsh taken from Newfoundland and Labrador regions show a value of 4.4 kJg−1 for Arctic cod and sandlance, with a higher energetic value of 8.4 kJg−1 for capelin (Lawson et al., 1998). Although values are not available for all prey items, capelin energetic values are higher than Arctic cod and sandlance are of comparable nutritional value. Thus, shifts in predator diets including these species may not alter their overall nutritional levels. This does not account for spatial availability of prey, as in reality changes in distributions may make prey unavailable. Rather if prey are available nutritional values suggest capelin and sandlance are suitable energetic substitutes to Arctic cod.

Key Uncertainties

The model identifies that certain groups can increase based on increasing food supply and decreasing predation. Many other factors may affect survival and should be studied carefully. The model does not account for factors which may affect breeding or reproduction as they are not well understood, but they should be considered when assessing future threats to any species group in the region. The most prominent changes to zooplankton from climate change include shifts in distribution with the general movement towards poles and earlier peaks in abundance (Richardson, 2008). Endemic species will have to compete with northward moving migrant species in addition to new invaders such as gelatinous zooplankton which may become prominent (Gradinger, 1995; Brodeur et al., 1999). Fish and invertebrates are predicted to have moderate local extinctions, invasions, and species turnover (Cheung et al., 2008). While Hudson Strait contains colder deeper water compared to HB, it may be acting as a thermal barrier to prevent temperate species from entering the area. Reduction of sea ice over

114 4.5. Discussion time in Hudson Strait is believed to have allowed killer whales to access Hud- son Bay (Higdon and Ferguson, 2009), thus potentially opening Hudson Bay and other high latitude regions to more temperate marine mammals shifting poleward (Kaschner et al., 2011). As Hudson Strait warms further, colder water currents may also be less effective at preventing invading zooplankton and fish species. For invasive species, thermal tolerances and quality of prey are important factors that should be incorporated into future models. Spa- tial components may alter results if prey are located only in specific areas within Hudson Bay. While the implications of climate change and removal of top predators are considerable (increased contaminants, invasive species, alterations to metabolic rates, changes in quality of prey items, etc.), we present the results of this model to be used as a tool to identify important stressors and their likely impacts to the Hudson Bay region.

General Conclusions

While there are multiple effects of climate change, some of which are accounted for within the model, the main results indicate the importance of harvest on marine mammal populations. Harvest rates must be decreased within the model in order to see environmental influences on higher trophic levels. As hunting has the greatest effect on marine mammal populations, efforts will need to focus on decreasing harvest limits, if managers want to maintain the structure of these marine mammal populations. Hunting pressure on vulnerable stocks (walrus, narwhal, eastern HB beluga, polar bears, and bearded seals) should be reduced in the immediate future to avoid extir- pation. The socio-economic ramifications to Inuit in Hudson Bay should be considered in the context of reducing or ending the harvest of these species. Yet, alternative protein sources are generally poor in quality, expensive, and do not benefit the health of northerners as do traditional foods (Loring, 1996; Tait, 2001; Freeman, 2005). Fishing may become more desirable since predator release by declining marine mammals may make fishing activities more lucrative than in the past. Model simulations highlight marine mammal and fish species (ringed

115 seals, western HB beluga, sandlance and capelin) able to increase under a doubling of present day harvest rates and changes to the food web. It is possible to consider that if harvest of vulnerable species is reduced, compensation could be allowed by increasing harvest on more stable species groups. Additional species specific modelling would be useful to assess the potential for changes in harvest levels at a finer scale. Furthermore, it may be a controversial option as the diet of Inuit in the region has been centered on marine mammals for thousands of years (Stewart and Lockhart, 2005). Community willingness to adhere to policy options and enforcement may be difficult. However, these issues need to be weighed heavily against the desire to prevent the extirpated of marine mammal populations.

4.6 Hudson Bay Biomass and Morality Figures

116 Polar Bear WHB ( 4.604e−04 ) SH Polar Bear ( 3.836e−04 ) Polar Bear Foxe ( 1.918e−04 ) Killer Whale ( 2.500e−05 ) 1.2e−20 1.2e−20 3.5e−05 0.00011 1.1e−20 1.1e−20 3.0e−05 2.5e−05 0.00010 1.0e−20 − − − − 1.0e−20 − − − − 2.0e−05 − − − − 0.00009 9.0e−21 9.0e−21 1.5e−05 1.0e−05 0.00008 8.0e−21 8.0e−21 5.0e−06 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Narwhal ( 0.00193 ) Bowhead ( 0.0109 ) Walrus N ( 0.00274 ) Walrus S ( 9.865e−04 ) 3e−12 0.0014 0.020 0.0025 0.0012 2e−12 0.0020 0.0010 0.018 1e−12 0.0008 0.0015 0.016 0e+00 0.0006 0.0010 − − − 0.0004 0.014 −1e−12

) 0.0005 0.0002 0.012 −2e−12 2 0.0000 0.0000 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Bearded Seal ( 0.0037 ) Harbour Seal ( 0.00105 ) Ringed Seal ( 0.0469 ) Harp seal ( 0.001 ) 0.09 0.0020 0.0020 0.08 0.0020 0.0015 0.07 0.0015 0.0015 0.06 0.0010 0.0010 0.05 0.0010 0.04 Biomass (t/km L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Beluga E ( 0.00207 ) Beluga W ( 0.0247 ) Beluga James ( 0.00146 ) Seabirds ( 0.065 ) 0.11 6e−04 0.040 0.0035 5e−04 0.10 4e−04 0.035 0.0030 0.09 3e−04 0.030 0.0025 0.08 2e−04 0.025 0.07 1e−04 0.0020 0.06 0e+00 0.020 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Arctic Char ( 0.416 ) Atlantic Salmon ( 0.148 ) Gadiformes ( 0.855 ) Sculpins/Zoarcids ( 0.383 ) 0.30 0.60 0.22 0.25 0.55 0.6 0.20 0.20 0.50 0.18 0.4 0.15 0.45 0.16 0.2 0.10 0.40 0.14 0.05 0.35 0.0 0.00 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2

117 Figure 4.9: Ending biomass by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation. Biomass for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Capelin ( 0.489 ) Sandlance ( 0.707 ) Sharks/Rays ( 3.180e−06 ) Other Marine Fish ( 0.375 ) 2.2 1.1e−06 0.9 1.2 2.0 1.0e−06 1.8 9.0e−07 0.8 1.0 1.6 8.0e−07 0.7 1.4 7.0e−07 0.6 0.8 1.2 6.0e−07 5.0e−07 0.5 0.6 1.0 0.8 4.0e−07 0.4 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Brackish Fish ( 0.0554 ) Cephalopods ( 0.227 ) MacroZooplankton ( 7.5 ) Euphausids ( 2.15 ) 0.09 0.35 3.5 0.08 14 12 3.0 0.07 0.30 10 2.5 0.06 0.25 8 0.05 2.0 ) L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 2 Copepods ( 4.01 ) Crustaceans ( 1.8 ) Other MesoZoopl. ( 1.21 ) MicroZoopl. ( 2.24 ) 2.6 2.0 7 2.4 3.5 2.2 1.8 6 3.0 2.0 1.6 5 1.8 1.4 2.5 1.6 1.2 4 2.0 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2

Biomass (t/km Marine Worms ( 5.93 ) Echinoderms ( 8.71 ) Bivalves ( 5.95 ) Other Benthos ( 3.14 ) 6.0 9 2.8 5.5 6.0 2.6 8 2.4 5.0 5.5 2.2 7 4.5 5.0 2.0 6 4.5 1.8 4.0 1.6 4.0 1.4 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Primary Production ( 8 ) Ice Algae ( 3.5 ) Ice Detritus ( 0.009 ) Pelagic Detritus ( 0.33 ) 20 0.008 0.7 18 2.5 16 0.007 0.6 2.0 14 0.006 0.5 12 1.5 0.005 10 0.4 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2

118 Figure 4.10: Ending biomass by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation. Biomass for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Polar Bear WHB ( 0.129 ) SH Polar Bear ( 0.154 ) Polar Bear Foxe ( 0.12 ) Killer Whale ( 0.151 ) 0.18 0.6 3.5 3.5 0.16 0.5 3.0 3.0 0.14 0.4 0.12 0.3 2.5 2.5 0.2 0.10 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Narwhal ( 0.0843 ) Bowhead ( 0.0211 ) Walrus N ( 0.172 ) Walrus S ( 0.0972 ) 3.5 0.030 5 4.5 3.0 4 4.0 2.5 0.025 3 2.0 3.5 1.5 0.020 2 1.0 3.0 0.5 1 0.0 0.015 0 2.5 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Bearded Seal ( 0.176 ) Harbour Seal ( 0.125 ) Ringed Seal ( 0.158 ) Harp seal ( 0.126 ) 0.50 0.45 0.18 0.18 0.40 0.14 0.16 0.16 0.35 0.12 0.14 0.14 0.30 0.12 0.25 0.10 0.12 0.20 0.10 0.10 0.08 Mortality (year -1) L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Beluga E ( 0.0658 ) Beluga W ( 0.0636 ) Beluga James ( 0.0873 ) Seabirds ( 0.37 ) 0.08 3.5 0.10 0.50 3.0 2.5 0.07 0.09 0.45 2.0 0.08 0.06 0.40 1.5 0.07 1.0 0.35 0.5 0.05 0.06 0.0 0.05 0.30 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Arctic Char ( 0.2 ) Atlantic Salmon ( 0.52 ) Gadiformes ( 0.47 ) Sculpins/Zoarcids ( 0.7 ) 1.1 0.26 0.65 0.7 1.0 0.24 0.60 0.9 0.22 0.55 0.6 0.8 0.20 0.50 0.5 0.18 0.45 0.7 0.16 0.40 0.4 0.6 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2

119 Figure 4.11: Ending mortality by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation. Mortality for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Capelin ( 1.7 ) Sandlance ( 0.85 ) Sharks/Rays ( 0.22 ) Other Marine Fish ( 0.932 ) 0.45 1.8 0.9 0.9 0.40 1.6 0.8 0.8 0.35 1.4 0.7 0.7 0.30 0.6 1.2 0.6 0.5 0.25 0.5 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Brackish Fish ( 3.5 ) Cephalopods ( 1.5 ) MacroZooplankton ( 1 ) Euphausids ( 3.3 ) 4.5 4.5 1.2 1.8 4.0 4.0 1.1 1.6 1.0 3.5 3.5 1.4 0.9 3.0 3.0 1.2 0.8 2.5 0.7 2.5 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Copepods ( 16 ) Crustaceans ( 3.6 ) Other MesoZoopl. ( 10 ) MicroZoopl. ( 15 ) 22 4.5 13 20 20 12 4.0 18 18 11 3.5 16 16 10 3.0 9 14 14 8 2.5 12 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2

Mortality (year -1) Marine Worms ( 0.6 ) Echinoderms ( 0.3 ) Bivalves ( 0.57 ) Other Benthos ( 2.5 ) 0.70 0.65 0.65 2.8 0.30 0.60 2.6 0.60 0.55 0.55 2.4 0.50 0.50 0.25 2.2 0.45 0.45 2.0 0.40 0.20 0.40 1.8 1.6 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 Primary Production ( 46.9 ) Ice Algae ( 46.2 ) Ice Detritus ( NA ) Pelagic Detritus ( NA ) 45 1.0 1.0 40 40 0.5 0.5 35 35 0.0 − − − − − 0.0 − − − − − 30 30 −0.5 −0.5 25 25 −1.0 −1.0 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 L1H1 L1H2 L2H1 L2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2 H1H1 H1H2 H2H1 H2H2

120 Figure 4.12: Ending mortality by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation.Mortality for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Chapter 5

Future Impacts of Fishing and Climate Change on the Antarctic Peninsula Marine Ecosystem

5.1 Synopsis

This chapter focuses on future simulations of the Antarctic Peninsula ecosystem, utilizing the past model simulations in chapter 3 to continue harvest and climate trends into the future. In keeping with chapter 4, data from global climate models representing future IPCC climate scenarios were used to continue model drivers into the future. These were combined with harvest scenarios representing 2007 levels (the last year of the past model simulation), and continued harvest at the quota level. Further development of harvest scenarios explores the eﬀect of harvesting sexually immature krill on the structure of the ecosystem. Declines in sea ice, detritus, ice algae, and krill caused by warmer climates are responsible for most of the changes to the food web. Future scenarios identify copepods as the potential dominant zooplankton, ﬁlling the ecological role of krill within the model. More extreme climate and harvest scenarios result in an overall increase in trophic level for the ecosystem, attributed to diets shifting away from the less available ice algae and detritus, and re-focusing on more available zooplankton species. Krill is a main component to many predator diets, and continuing the harvest of krill may does directly cause large declines in predators, but

121 5.2. Introduction it will contribute to decreasing a continually strained resource in the future.

5.2 Introduction

At the present time, rapid changes in climate are occurring in a short geological time frame, as indicated by unprecedented levels of methane and

C02 in ice cores when compared to the last 420,000 years (Petit et al., 1999). Increased greenhouse gases are contributing to the overall warming of earth, with larger than average temperature increases observed at the Antarctic peninsula (Anisimov et al., 2001; Hansen et al., 2006a). Global, large scale changes will occur with continued warming of the Antarctic and the rest of the planet. Increased Antarctic temperatures have been linked to global sea level on geological time scales, through ice volume changes (Rohling et al., 2009). The West Antarctic Ice Sheet (WAIS), covering the western portion of the continent, would cause global increases in sea level 4-6m if it collapsed (Oppenheimer, 1998). While there is some thickening of the WAIS in the west, it is likely as a whole the ice sheet in thinning (Rignot and Thomas, 2002). Changes to the WAIS are thought to be larger than for the East Antarctic Ice Sheet (Bindschadler, 1998), which is said to be stable (Sug- den et al., 1993). Increases in greenhouse gases and temperatures globally, and at the Antarctic peninsula, have resulted in the recent loss of major ice sheets, many at rates much faster than predicted. For example, the Wordie, Wilkens and Larsen ice shelves at the Antarctic peninsula have all shown major reductions and collapses starting in the 1960s (Doake and Vaughan, 1991; Rott et al., 2002; Rignot et al., 2005). The loss of sea ice at the Antarctic Peninsula has been shown to alter the ﬂow of nutrients to top predators. Years with high sea ice (high-salinity water) favor diatom blooms where energy is then transferred to krill Eu- phausia superba and then further up the food chain (Marschall, 1988; Loeb et al., 1997; Moline et al., 2000; Atkinson et al., 2004). Low ice years (low salinity and increased glacial meltwater) favor smaller cryptophytes, which are then eﬃciently grazed by salps Salpa thompsoni (Moline et al., 2000, 2004). Declines in krill have been linked to poor reproductive success for

122 5.2. Introduction

Adelie penguins and Antarctic fur seals Hofmann et al. (1998); Brierley and Reid (1999), however declines likely occur in other undocumented species as well. In addition to the stress of a changing environment, krill (Euphausia superba) is harvested primarily for aquaculture feed, although products for human consumption and pharmaceuticals do exist (Nicol and Endo, 1999; Kawaguchi and Nicol, 2007). The quota for krill has increased from 1.5 to 4 million tonnes between 1991 and 2000, although CCAMLR (Commission for the Conservation of Antarctic Marine Living Resources) applies a precautionary approach to the management of Antarctic krill such that only roughly 9.1% of the total biomass may be harvested (Hewitt et al., 2002; Nicol and Foster, 2003). This value is intentionally set low to ensure that predator demands are met, and krill biomass does not drop below 20% of the unexploited biomass (Hewitt et al., 2002). However, issues of spatial overlap indicate that krill predators and fisheries may be in competition regarding the timing and location of acquiring krill (Reid and Croxall, 2001; Hewitt et al., 2004). The subdivision of catches into smaller areas or ”Small Scale Management Units” has been proposed decrease fishery pressure in concentrated areas (Hewitt et al., 2004). There is a discrepancy between the actual harvest levels of roughly 100,000t compared to the potential quota levels of 4.89 million tonnes (Nicol and Foster, 2003). A ”trigger level” of 620,000t exists for the Atlantic sector, meaning if this level is reached within any fishing area (such as all sub-areas within area 48), catches within the area should be further subdivided (Antarctic and Southern Ocean Coalition, 2010). Krill catches in 2010 increased to 210,000t, roughly double the 1994-2009 catches (Nicol et al., 2012). Recent CCAMLR documents indicate the trigger level needs re-assessment as it was established in 1991 because krill fishing effort is excessively concentrated in coastal areas (Antarctic and Southern Ocean Coalition, 2010). Recent expansions in krill based products for pharmaceuticals, health food products and aquaculture feed are likely to increase pressure on the fishery (Nicol et al., 2012). Krill in the aquaculture market is expected to continuing growing with increased demand for krill in aquaculture feeds

123 5.3. Methods

(Gascon and Werner, 2006). Because catches are lower than quota levels the aquaculture industry views krill as having an unexploited biomass (Olsen et al., 2006). It has been demonstrated it is a suitable substitute for traditional fish meal for farmed Atlantic salmon (Olsen et al., 2006), and contains the desirable long-chain omega-3 polyunsaturated fatty acids that consumers favor in fish products (Naylor et al., 2009). The number of krill- related patents has increased every year from 1976-2008, with most patents related to krill products for human consumption, and Japan as the country having lodged the most patents (Nicol and Foster, 2003). Recreation of the past Antarctic peninsula ecosystem through the use of an ecosystem model suggests that environmental factors have been more significant than harvest in determining krill and krill-predator biomass (Chap- ter 3). The model shows that increasing the harvest of krill from past levels of roughly 100,000 tonnes for area 48.1 to the quota of 625,000 tonnes (He- witt et al., 2002) does not bring about significant changes to the trophic structure of the ecosystem. It should be noted, however, that harvest rates are only assessed on a temporal scale in this model and trophic structure will likely show higher sensitivity to harvest on a spatial scale. Since increasing harvest levels in combination with changes in climate will potentially alter the ecosystem, scenario simulations using this ecosystem model have been employed to assess potential future states of the ecosystem. As some life stages of krill are linked to sea ice for food and protection (Marschall, 1988; Daly, 1990), and krill are directly harvested, this central link in the ecosystem will face multiple stressors. This modeling exercise aims to identify vulnerable linkages in the ecosystem in addition to identifying the strongest factors for change.

5.3 Methods

Model Structure

Using the previously constructed EwE model for the Antarctic Peninsula (chapter 3), various scenarios of climate change and harvest levels were

124 5.3. Methods simulated to assess the impacts on the ecosystem. Continuing from the past model, where known trends were recreated, this chapter focuses on increasing stressors and the trophic implications. Some minor adjustments to the model (chapter 3) were made in light of more recent literature. Immigration rates for chinstrap and gentoo penguins were removed based on longer timescale data indicating that their populations are not increasing at the Antarctic Peninsula (Trivelpiece et al., 2010), contrary to the previous belief that they were increasing partly due to immigration from other regions (Fraser et al., 1992). The past model was forced with environmental drivers and harvest. Har- vest rates were taken from recorded CCAMLR (Commission for the Conser- vation of Antarctic Marine Living Resources) catches in the past (CCAMLR, 2008b). Environmental drivers were selected as forcing functions for primary producer groups in addition to mediation functions12. Ecosim simulations used the Ecopath with Ecosim software (Christensen et al., 2005; Buszowski et al., 2009), where temporal changes in biomass are calculated using equations 2.3 in chapter 2 at each time step in the model.

Once simulations were completed the trophic level of the ecosystem (TLE) and the trophic level of catches (TLC ) were calculated using equations 2.6 and 2.7 from chapter 2. Ice cover is included in the model as a driver of the ice algae and diatom functional groups. Ice algae refers to the algal cells frozen within the sea ice that remain overwinter as a potential food source for some stages of krill (Marschall, 1988; Arrigo et al., 1997). Diatoms can also survive within the sea ice, but were given their own functional group due to their importance to zooplankton species. They are also favored in cooler years, contributing to pelagic blooms during spring melts (Legendre et al., 1992; Varela et al., 2002; Garibotti et al., 2003; Moline et al., 2004). SST (sea surface temperature) was chosen as a driver for cryptophytes and other phytoplankton functional

12Forcing functions act as a multiplier to trends over time for the producer groups, while mediation functions act as an indirect link between species groups. For example in the past model, the presence of ice reduces the vulnerability of some krill stages to predators, as they have been observed to hide in ice crevasses (Marschall, 1988).

125 5.3. Methods groups, primarily to fit the salp functional group better, whose abundance patterns are linked to warmer waters (Atkinson et al., 2004; Pakhomov and Froneman, 2004). Increased biomass of cryptophytes has been linked to warmer years and lower salinity water (Moline et al., 2000, 2004). For the past model, SST (sea surface temperature) and ice cover (% of model area covered) were used to force different primary producer groups and mediation functions for krill and salps. Continuing the model into the future was accomplished through extending environmental time series. Data was extracted from future climate models and combined with past model data for SST and ice cover to provide an extended dataset covering 100 years. Combining past simulations with different levels of future model forcing, allowed simulations previously ranging from 1978-2007 to extend to 2077.

Environmental drivers

To assess future ecosystem state two climate scenarios were combined with three harvest scenarios. The climate scenarios are based on low and high climate projections for the Antarctic Peninsula using the global GDFL CM2.1 coupled model (GFDL, 2010). For the ’Low’ climate scenario the IPCC Constant 2000 emissions scenario was used, where greenhouse gas emissions are set to the year 2000 emission levels with global changes to environmental parameters considered conservative. While this target has already been surpassed in reality, it serves as a conservative estimate to system dynamics. The ’High’ climate scenario for the model employed the A1B IPCC scenario, whereby future emissions are a result of a balanced energy future (IPCC, 2000). As with chapter 4 the A1F1, B2 and A1 scenarios were considered before selecting the A1B as the ’High’ climate change scenario. However, due to challenges in accessing data and reliability of climate model outputs, the A1B scenario gave more extreme ice loss in future simulations, so it was selected. Ice and SST trends for past and future states are presented in ﬁgure 5.1. The past ice and SST data extracted from the global HadISST (Hadley Centre Sea Ice and Sea Surface Temperature model) at the British

126 5.3. Methods

Atmospheric Data Centre (BADC, 2010), and combined with the future data to allow the model to run from the past through present day and into the future. In order to assess the potential for increased variance in future environmental factors, two levels of variances were applied to each climate scenario (Low and High). A constant variance scenario (Low) used variance based on past data to assume variance in future data is not any higher or lower than past changes. Using a normally distributed multivariate covariance matrix generated from past data, variance was applied to the future time series of SST and ice cover. 100 draws of variance created 100 scenarios with the same mean values to allow the model to be run for 100 simulations for each scenario. Next, for the variance was doubled (High variance), while mean trends remained the same to account for the possibility of increased variance in the future. Labels for the variance in the model are referred to numerically as 1 or 2, so a Low climate scenario with variance based on past values would be referred to as the L1 climate scenario while doubling the variance would be referred to as the L2 scenario.

A B 1978−1982 2074−2077 Low 1978−1982 2074−2077 High 2074−2077 Low 2074−2077 High

o SST ( C ) Ice Cover (%) 0 1 2 3 4 0 10 20 30 40 50

2 4 6 8 10 12 2 4 6 8 10 12 Month Month Figure 5.1: Sea ice and SST data used for model simulations. Past values were extracted from the HadISST global model (BADC, 2010). Future ice and temp data was extracted from the GDFL CM2.1 coupled model (GFDL, 2010).

127 5.3. Methods

Harvest Levels

Each of the climate scenarios was combined with krill and fish harvest at varying levels. The first scenario, the ’Low Harvest’ (H1) scenario combines past harvest trends with future catches constant at 2007 harvest levels for all species harvested (table 5.1). The catch from 2007 is kept constant from 2008-2077 to identify the long term effects of current harvest levels. In some cases, where fish species were not harvested every year, the highest value identified from a five year time span (2003-2007) (CCAMLR, 2008b) was continued into the future. In addition, harvest for some fish species no longer occurs. In these cases, no catches were used in future scenarios. Krill are harvested every year, therefore the 2007 value remained constant for the ’Low Harvest’ scenario. The second harvest scenario considers the effects of higher landings for both fish and krill. Under this scenario, krill catches are increased to operate at the current quota level, while fish catches are doubled from their 2007 values (or values used in the ”Low Harvest” scenario). This ”High Harvest” (H2) scenario, is then further broken down into two subroutines. The first (H2a) assumes all krill catches are taken from the adult krill stage and the second (H2b) splits the krill catches into the adult and juvenile stages. In reality both juvenile and adult krill are harvested with larger krill being more valuable. Quality of harvested krill is graded by length, with krill >45mm earning the highest value and krill <35mm earning the lowest (Ichii, 2000). Length of krill in Polish catches in southwest Atlantic sector of the Antarctic ranged from 25-60mm in the late 1990s, with a majority of the catches >35mm (Jackowski, 2002). For the H2b scenario, 75% of catches were assumed to be from the adult krill group, while 25% were from the juvenile krill group, to explore the effects of harvest at different life stages. Catches and harvest mortalities for each scenario are presented in table 5.1.

128 Table 5.1: Summary of harvest values and hunting/fishing mortalities used for the initial Ecopath model (1978), and future hunting scenarios: H1 where catch and effort are constant to 2007 values, and H2 where catches and effort are doubled from the 2009 values. Fishing mortality for future scenarios is calculated using the 2007 biomass for each species group.

Species Group Catch t · km−2 Fishing Mortality (y−1) 1978 H1 H2a H2b 1978 H1 H2a H2b krill adult 0.055 0.100 0.923 0.692 0.006 0.016 0.146 0.110 krill juvenile 0.018 0.033 - 0.231 0.001 0.002 - 0.013 C. gunnari 1.00E-05 2.53E-05 5.06E-05 5.06E-05 3.45E-05 1.64E-04 3.28E-04 3.28E-04 N. gibberifrons 1.00E-05 2.98E-06 5.96E-06 5.96E-06 1.23E-05 4.48E-06 8.97E-06 8.97E-06 P. antartcicum 1.00E-05 2.08E-05 4.16E-05 4.16E-05 8.00E-06 2.16E-05 4.32E-05 4.32E-05 Other Iceﬁsh 1.00E-05 4.46E-06 8.92E-06 8.92E-06 2.97E-05 1.70E-05 3.40E-05 3.40E-05 lg noto 1.00E-05 1.49E-05 2.98E-05 2.98E-05 1.69E-05 3.06E-05 6.13E-05 6.13E-05 sm noto 1.00E-05 1.19E-05 2.38E-05 2.38E-05 2.93E-05 4.06E-05 8.12E-05 8.12E-05 myctophids 1.00E-05 - - - 5.41E-05 - - - other pelagics 1.00E-05 1.49E-06 2.98E-06 2.98E-06 2.04E-05 3.76E-06 7.52E-06 7.52E-06 toothﬁsh 1.00E-05 - - - 2.16E-04 - - - 129 5.4. Results

Table 5.2: Simulations of varying levels of climate and hunting. Scenario names indicate levels of hunting and climate. First letter indicates either a Low (L) or High (H) climate scenario followed by the variance applied to the climate data (either past variance (1) or double the past variance (2)). The second letter indicates the level of hunting applied to the simulation; H1 for constant hunting at the 2007 levels, or H2 for harvest at quota levels.

Climate Scenario Variance Hunting Scenario Scenario Abbreviation Low Normal Constant 2007 L1H1 Low Normal Double adult L1H2a Low Normal Double adult/juvenile L1H2b Low Doubled Constant 2007 L2H1 Low Doubled Double adult L2H2a Low Doubled Double adult/juvenile L2H2b High Normal Constant 2007 H1H1 High Normal Double adult H1H2a High Normal Double adult/juvenile H1H2b High Doubled Constant 2007 H2H1 High Doubled Double adult H2H2a High Doubled Double adult/juvenile H2H2b

5.4 Results

General Results

All future scenarios reveal declines for ice-associated producers ice algae and diatoms. The other phytoplankton group increases in all future scenarios with cryptophytes showing mixed results. Declines in the biomass of cryptophytes under future scenarios is attributed to increased predation mortality rates, caused primarily by salps. Detritus shows moderate declines ranging from 58% to 70% for the L1H1 and H2H2a scenarios. Decline in detritus is caused by declining primary production, and overall lower biomass of the ecosystem, both of which feed into the detritus pool. Total primary production declines 63% for the L1H1 scenario and 68% for the H2H2a scenarios, with all scenarios falling in the range of 54% to 75% declines. Figure 5.2 identiﬁes all changes to functional groups by scenario in reference to the original 1978 starting biomass.

130 5.4. Results

Increasing the effects of climate change does show some alterations to the ecosystem. The H2b harvest scenario (75% harvest of adult krill and 25% harvest juvenile krill) has the largest confidence intervals under each climate scenario for both biomass and mortality. Most species across all trophic levels show largest CI for H2b harvest scenarios, especially when combined with High variance in climate models (L2 and H2 scenarios). High variance climate scenarios coupled with harvest of juvenile krill cause large ranges of future ecosystem states, as CI for some groups are larger than the mean biomass for some groups (diatoms, cryptophytes, copepods, micro- zooplankton, and krill groups) (see figure 5.15 for all scenario results). It is important to note the harvest of adult krill is higher for the H2a harvest scenario, therefore removal of adult krill is not the cause of the large variance. The highest biomass of krill (adult and juvenile groups combined) in future scenarios is under the L1H1 and L2H1 scenarios, in which harvests are the most conservative of any future scenarios combined with less variance in environmental drivers. However, under these scenarios declines of combined adult and juvenile groups are roughly 75% from their starting biomass, indicating while the scenario structure may be conservative, the results and impacts to the ecosystem are not. Results are presented by trophic groupings with key species highlighted. For these selected species scenarios L1H1, H2H2a and H2H2b are represented graphically to highlight changes to the most important contributors to diet and mortality.

Changes in Ecosystem Biomass and Trophic Levels

Overall there are large declines in the total biomass of the ecosystem compared to the starting values (table 5.3). The Low climate scenarios show higher ending biomass than the High climate scenarios with the exception of the H1H2b and H2H2b scenarios. This is due to further declines of species caused by increased climate scenarios. The higher biomass in the H1H2b and H2H2b scenarios is an artefact of the large variation in biomass the lower trophic levels. Trophic Level (TL) of the ecosystem shows slight increases

131 5.4. Results in most future scenarios when compared to past and present values. This is due to the fact that the TL of most functional groups increases at varying degrees in the future simulations. The loss of producers may alter diets to shift to higher trophic levels when primary production is not available, thus increasing the TL of organisms. The TL of catches decreases from the starting value of 3.39 to a value of 2.34 for 2007 (the ending year of the past model), before continuing to increase under all future scenarios. The high starting value is a reflection on the fish contribution to catches in the past model. A combination of krill and fish were harvested, with fish groups having higher trophic levels than krill (adult krill TL=2.53, juvenile krill TL=2.25 for 1978). At the end of the past simulation, TL of catches is at the lowest value, as krill contributed largely to the total of all species harvested thus reducing the overall TL of catches. In future scenarios, krill continue to be a major contributor to the total catch, however TL of catches is increasing. This is due to the overall TL of krill increasing. Mean TL of adult krill as averaged over the last 10 years for each scenario ranged from 2.71-2.95, while juvenile krill ranged from 2.40-2.75 indicating krill are feeding at a higher trophic level. This is most likely explained by a reduction of primary production (primarily ice algae and diatoms) in the diets of krill at various stages, being replaced by higher TL zooplankton species, such as copepods or micro-zooplankton.

132 5.4. Results

Table 5.3: Trophic level of ecosystem (TLE) and catches(TLC ) for the Eco- path model (1978) and each simulation. Results presented are averages values for the last 10 years of each simulation. Total biomass and total catch are presented in t · km−2 for all species within the ecosystem.

TLE TLC Biomass 1978 1.91 3.39 209.73 2007 1.98 2.34 134.97 L1H1 2.02 2.68 119.56 L1H2a 2.05 2.78 130.06 L1H2b 2.01 2.74 113.76 L2H1 2.02 2.64 117.50 L2H2a 1.98 2.79 107.90 L2H2b 2.05 2.80 161.02 H1H1 2.10 2.74 99.25 H1H2a 2.07 2.88 102.21 H1H2b 2.15 2.89 144.23 H2H1 2.08 2.74 98.82 H2H2a 2.05 2.87 105.33 H2H2b 2.12 2.86 154.31

133 134 enboaso h nigvle o ahseai saeae vrtels 0yaso h simulation. the of years 10 last the the as over presented averaged are as values scenario Represented each scenario. for future values each ending for the group of species by biomass biomass mean in Changes 5.2: Figure H2H2b H2H2a H2H1 H1H2b H1H2a H1H1 L2H2b L2H2a L2H1 L1H2b L1H2a L1H1

Killer Whales S Elephant Seals Toothfish Sperm Whales Leopard Seal Ross Seal Weddell Seal Gentoo P Chinstrap P Emperor P Other Pelagics Ant Fur Seals Macaroni P Other Icefish Flying birds Deep demersals Lg Deep demersals Sm Adelie P Fin Whales Crabeater Seal Blue Whales C. gunnari Cephalopods Humpback Whales Shallow Demersals Sm Nototheniidae Lg Nototheniidae Minke Whales P. antarcticum Myctophids N. gibberifrons Other Arthropod Echinoidea Krill Adult Ophiuroidea Worms Cnidaria Crinoidea Crustecea Asteroidea Krill Juvenile Macro-Zoopl Brachiopoda Mollusca Urochordata Copepods Bryozoa Porifera Hemichordata Holothuroidea Krill Larvae Krill Embryo Micro-Zoopl Salps Cryptophytes Diatoms Ice algae Other Phytopl Detritus -50.1% to - 75% to -50.1% -25.1% to -50% to -25.1% 25.1% to 50% to 25.1% 75% to 50.1% 0.1% to 25% 0.1% to -25% to 0% to -25% < -75.1% < > 75.1% 75.1% > 5.4. Results

Producers and Detritus

Total primary production declines in all future model scenarios. Although there are increases in the ’other phytoplankton’ group, all other producers decrease. This is expected for ice algae and diatoms as they are driven with a sea ice forcing function. However, as cryptophytes and other phytoplankton groups are driven with SST, we would expect the cryptophytes to increase. Cryptophytes had the smallest starting biomass of all producers (in 1978). Increases in salps, who feed efficiently on these smaller producers contribute to large increases in total mortality for cryptophytes. Even though the drivers forcing this group are increasing in magnitude, effects of predation mortality are greater, causing overall declines (figure 5.18). Detrital groups also show declines, with the general trend of biomass decreasing further from the Low to High climate scenarios. This is caused by less contributions to detritus from producers and other organisms as their biomasses have declined.

Zooplankton

Copepods

For copepods, the biomass decreases slightly (13%) for the L1H1 scenario, although it increases by 45% and 193% for the H2H2a and H2H2b scenarios. For reference the ending biomass for the H2H2b scenario averaged 57t·km−2 compared to the starting biomass of 19t·km−2. Mortality decreases under all scenarios from the starting values due to decreases in predation, as copepods are not harvested (ﬁgure 5.3). Declines in the L1H1 scenario are caused by the declines in ice algae, diatoms, and micro-zooplankton prey items. While micro-zooplankton and diatoms remain near absent in the H2H2a scenario, copepods increase. This is a result of further declines in predation from krill groups, and a slight increase in ice algae contribution to the diet. Under the L1H1 scenario, there are higher biomasses of other functional groups, thus higher predation levels from these groups, in conjunction with less ice algae available to copepods. Under the H2H2a scenario the contribution

135 5.4. Results of ice algae increases when compared to the L1H1 scenario. This increase continues with the H2H2b along with further declines in krill and other predators resulting in a tripling of biomass. Under the H2H2b scenario predation mortality caused by both adult and juvenile krill is greatly reduced (100% declines for both groups). This is especially important for the juvenile krill as they have the highest predation mortality on copepods at the start of the model. For copepods the L2H2b and H2H2b scenarios have high variance in the biomass results (ﬁgure 5.15), which is an important reason for the variation in higher level species such as some ﬁsh and marine mammals.

Krill

Future impacts to krill populations come from two sources in the model. First, there are changes caused by food web interactions (declines in major contributors to the diet; ice algae and diatoms). Second, an ice-mediating function increases the vulnerability of larval and juvenile krill to predators as sea ice decreases (see appendix J for full details). As the krill embryo group represents a non-feeding stage before krill rise to the surface to feed, the diet of this group was considered as imported to the model. This allows for large increases in biomass of this group, based on the adult (sexually mature) population. A similar, but lower increase holds for the larval krill group. While there are almost certainly additional environmental factors contributing to the biomass of these groups, the increase in biomass under model scenarios should be considered optimistic. However, even as these groups increase, older stages of krill are not as successful in the simulated future. Changes in juvenile krill for the L1H1 scenario are caused by bottom- up changes in the ecosystem. A decline in biomass by 77% occurs in this scenario, while total mortality declines slightly (ﬁgure 5.4). Declines in ice algae, diatoms and copepods as prey items are important. Ice algae is also included as a mediation function whereby its presence reduces juvenile krill’s vulnerability to predators. However, as total mortality has not increased under this scenario, this is likely not an important contributor to

136 5.4. Results the decline. Under the H2H2a scenario juvenile krill biomass declines by 90% of the starting value (a further 13% from L1H1). There are no catches of juveniles in this scenario, but further reduction of adults is the reason for further declines. Total mortality is lower in this scenario, however adult krill biomass is further reduced. In the H2H2b scenario, juvenile krill continue to decline further to a 94% reduction from the starting biomass, even though copepods are increasing and ice algae declines are less than in previous scenarios. The further reduction is a result of direct harvest of this group with a larger ﬁshing mortality (0.54y−1 compared to starting value 0.01y−1) and an increased total mortality. Total mortality for juvenile krill and adult krill are highest in harvest scenarios H2b (with the exception of L1H2b). In the Low climate scenario juvenile krill are able to withstand an increase in harvest. However the increased variability of the Low climate scenario and the high reduction of ice in the High climate scenario coupled with the direct harvest of juveniles increases mortality on juvenile krill more than double compared to the starting value. This is also reﬂected in the adult group, whereby the mortality is higher in these scenarios, even when less adult krill is harvested. There is less recruitment into the adult phase, as more juveniles are being harvested increases the mortality on the adult krill stage in these scenarios.

Adult Krill

For adult krill declines in biomass of 75% caused from the L1H1 scenario are a result of bottom up changes in the ecosystem. While the increase in ﬁshing mortality appears to be large at 298% (increase from 0.01y−1 to 0.04y−1), total mortality in this scenario decreases from the starting value of 1.54y−1 to 1.32y−1 indicating changes are a result of bottom-up changes in the food web (ﬁgure 5.5). This is also in part by declines in lower stages of krill, which are impacted by sea ice through mediation functions. Under the H2H2a scenario adult krill biomass is further reduced as total mortality increases due to catches operating at current harvest quotas (with all catches from the adult krill groups). An important predator group, macro-zooplankton,

137 5.4. Results also increases. However predation mortality from this group is 10% lower at the end of the scenario than the 1978 starting value. The biomass of adult krill declines 92% under this scenario. However total mortality continues to increase under the H2H2b scenario, where 25% of the catches at quota level are taken from the juvenile krill group. Biomass of adult krill declines by 98%, even though there are increases in copepods and other phytoplankton, and a slight rebounding of ice algae under this scenario, and less adult krill are being harvested. Yet, the impact of harvesting juveniles increases the total mortality, causing the further decline of the adult biomass.

Salps

Salps show increasing biomass at varying levels in all future scenarios (ﬁgure 5.6). Prey items for salps are favored through the use of forcing functions; SST was used to force the cryptophytes and other phytoplankton groups, and a mediating function allowing greater foraging area for salps as sea ice declines. These groups are favored by increases in SST and contributed 65% of the total diet to salps at the start of the model. For the L1H1 scenario salps increase in biomass 75%. Although there is an overall slight increase in predation mortality, biomass is able to remain high. While salps generally contribute very little to the diets of other functional groups, their increasing biomass allows for increased predation, as other food items of predators are decreasing. Biomass patterns by scenario show increasing biomass as krill harvests increase from H1 to H2a to H2b (see ﬁgures 5.14 and 5.15). In the H2H2a scenario, biomass of salps is lower (although still increasing). Total mortality is relatively constant, but changes to the diets include; slightly more cryptophytes available, more copepods available, and less other phytoplankton. The ’other phytoplankton’ group contributes 35% of the total diet in the Ecopath model, but for future simulations the contribution can easily reach over 85% of the total diet. The lower salp biomass in the H2H2a and H2H2b scenarios is heavily reliant on the other phytoplankton biomass.

138 L1H1 Copepods H2H2b Copepods Adult Krill H2H2a Copepods M-100% Juvenile Krill Adult Krill Juvenile Krill M-100% -98% -94% -75% -77% Adult Krill M-89% M-84% Juvenile Krill -92% -90% M-65% M-65% Copepods Copepods -14% Copepods

+193% Micro- +45% Other Zooplankton Other Micro- Micro- Phytoplankton Zooplankton -100% * Phytoplankton Zooplankton Other -100% * Ice Algae -100% * Phytoplankton Diatoms Ice Algae Diatoms Diatoms +45% Ice Algae -100% * -74% +110% -100% * -100% * -70% -62% +24%

(a) S=29.35, E=16.86 (b) S=29.35, E=9.92 (c) S=29.35, E=7.66

Figure 5.3: Changes in biomass for copepods with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100%. *Indicates ending biomass is reduced by 100%. 139 Juvenile Krill L1H1 Juvenile Krill H2H2a Juvenile Krill H2H2b +6% Adult Krill +87% Macro- +52% Adult Krill Adult Krill +129% Zooplankton -75% Macro- +144% Catches -92% Macro- Zooplankton Catches -98% M -59% Zooplankton Catches M+1% -100% * M-87% M-100% F+726% M+2% M-7% Juvenile Krill F -100% F +3889% Juvenile Krill -77% Juvenile Krill -90% -94%

Copepods -14% Copepods Ice Algae Copepods Ice Algae Ice Algae -74% Other Diatoms +45% Phytoplankton -70% -62% Diatoms Other +193% Other -100% * Phytoplankton Diatoms Phytoplankton -100% * -100% * +110% +45% +24% (a) S=0.94, E=0.83 (b) S=0.94, E=0.72 (c) S=0.94, E=3.68

Figure 5.4: Changes in biomass for juvenile krill with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality and catches are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100%. 140 L1H1 Adult Krill H2H2b Adult Krill +6% H2H2a Adult Krill +129% Crabeater +52% Macro- Seals Zooplankton Crabeater +585% Crabeater -98% Seals Macro- Macro- Seals Zooplankton Catches Zooplankton -99% -99% M-96% -1% Catches M+3% Catches M-99% M-99% F+298% M-10% M-3% -3% Adult Krill F+9349% F+4959%

Adult Krill Adult Krill -75% -92% -98% Copepods -14% Copepods Ice Algae Copepods Ice Algae Ice Algae -74% Other +45% Diatoms -70% Phytoplankton Other -62% Other -100% * Diatoms +193% Phytoplankton Diatoms Phytoplankton -100% * -100% * +110% +45% +24% (a) S=1.54, E=1.32 (b) S=1.54, E=3.37 (c) S=1.54, E=4.30

Figure 5.5: Changes in biomass for adult krill with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality and catch are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100%. *Indicates ending biomass is reduced by 100%. 141 L1H1 Salps H2H2a Salps H2H2b Salps Cnidarians +1% Flying Birds Cnidarians Cnidarians Cephalopods Flying Birds -7% Flying Birds -3% M+12% -62% -47% Cephalopods M-31% -68% -52% Cephalopods M-40% M-1% -50% M+5% M-31% M-24% -14% +75% M-7% M-30% +37% +49%

Salps Salps Salps Copepods -14% Cryptophytes Copepods Cryptophytes -38% Copepods Other Cryptophytes +45% +1% Phytoplankton -31% Other Other Diatoms Diatoms Diatoms Phytoplankton Phytoplankton -100%* -100%* -100%* +193% +110% +24% +45% (a) S=10.00, E=11.69 (b) S=10.00, E=11.53 (c) S=10.00, E=11.67

Figure 5.6: Changes in biomass for salps with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100%. *Indicates ending biomass is reduced by 100%. 142 5.4. Results

Other Zooplankton

For the micro-zooplankton group biomass decreases due to increased predation mortality for all future scenarios. There is High variation in the L2 (Low climate double variance) scenarios, although mean biomasses remain quite low. Macro-zooplankton biomass increases in future scenarios while total mortality declines. The increase in biomass is possible as the contribution of other phytoplankton in the diet increases. The cephalopod group also shows declines in biomass and mortality indicating declines are driven by a lack of prey.

Benthos

All benthic groups show lower mortalities in all future model scenarios, due to declines in higher trophic levels. While most species decline in biomass due to reductions in detritus and ice algae, there are a few species that show increases in some scenarios (mostly the H2b scenarios). Cnidarians, crustaceans, other arthropods and echinoderms all have high contributions of other phytoplankton and macro-zooplankton to their diets at the end of future scenarios which allows for increases in biomass, as these groups are increasing. The remaining benthic groups have diets weighted more heavily on detritus, and thus are impacted through bottom up changed such as declines in detritus. Even though predation mortality is lower for these groups, prey items are reduced enough in the model to cause decreases in future biomass.

Fish

Myctophids

Myctophids are one of few groups to increase in biomass under future scenarios. Under all future scenarios total mortality decreases indicating a predator release may be causing the biomass to increase. However, under the L1H1 scenarios prey items are all declining as well, while biomass increases by 3% (ﬁgure 5.7). Less important prey items at the start of the model such

143 5.4. Results as macro-zooplankton increase in contribution to the diet of myctophids by more than double in future scenarios. In addition, myctophids are predated upon by many higher organisms, most of which decline substantially in the future. The relief in predation coupled with increases in copepods allows this group to increase. For the H2H2a scenario, a further decline in total mortality is observed in the model, with biomass increasing 70%. While adult krill and molluscs continue to decline, there is a slight rebounding of crustaceans, and an increase in copepods in conjunction with an increase in copepod contribution to the total diet. For the H2H2b scenario biomass of myctophids increases 176% from the starting value. This is caused by the large increase in copepods, which contributes 61% of the diet at the end of the scenario compared to 25% at the start (this is also the highest contribution of copepod to the diet compared to other scenarios). Crustaceans also show an increase in biomass as their diet in the future is heavily weighted on other phytoplankton and macro-zooplankton in this scenario in addition to the increase in the contribution to the diet. Crustaceans and copepods make up more than 86% of the ending diet. While total mortality is slightly higher than other scenarios (0.96 compared to 0.91 for L1H1, and 0.83 for H2H2a), it is still lower than the starting mortality of 1.35. In the model myctophids are able to increase through a combination of decreased predation, and increasing the contributions of available prey to the diet.

Deep Demersals Large

The large deep demersal group shows declines, although not as much as other ﬁsh groups. This is primarily due to a diet based more heavily on benthic species which do not decrease as much as pelagic species. A 9% decrease of large deep demersals for the L1H1 scenario further declines to 42% for the H2H2a scenario before increasing slightly to a total decline of 18% for the H2H2b scenario. Under the L1H1 scenario, major prey items decrease in biomass ranging from 27% for crustaceans to 77% for juvenile krill. Total mortality also decreases in this scenario as main predators are declining as well. The H2H2a scenario identiﬁes further decline of predators and total

144 5.4. Results mortality in conjunction with larger declines in prey items with the exceptions of crustaceans. Crustaceans do increase in this scenario as previously mentioned due to the high percentage of other phytoplankton and copepods in their diets. For the H2H2b scenario the decline in large deep demersals is only 18% from the starting biomass, despite a slightly higher mortality than the other scenarios discussed. An increase in macro-zooplankton contribution to the diet occurs in the H2H2a and H2H2b scenarios. The rebounding in biomass for the H2H2b scenario is due to higher biomasses of important contributors to the diets (deep demersals small, macro-zooplankton and P. antarcticum) although they were not large contributors to the diet at the start of the simulation. The shift in the diets combined with higher biomasses of copepods and crustaceans in the H2H2b scenario prevent the larger declines as shown in scenario H2H2a.

Toothﬁsh

Toothfish show large declines across all scenarios ranging from 37% for the H2H2b scenario to 55% for the L1H1 scenario (figure5.9). Mortalities for all future scenarios are lower than the starting value indicating changes to toothfish are caused by bottom-up rather than top-down interactions. Catches for toothfish were set to 0 for the future simulations as they had only been harvested sporadically in the past (CCAMLR, 2008b). The H2H2b scenario has the highest biomass of toothfish when comparing the three scenarios, due to to increases in small notothenoiidae biomass (10%), and less severe declines of other important prey items. These four prey items remain the highest contributors to the diet for future scenarios with crustaceans as fifth, whose biomass also increases in the H2H2b scenario.

145 H2H2a Myctophids H2H2b Myctophids L1H1 Myctophids Cephalopods Cephalopods Cephalopods Other Flying Birds Flying Birds -49% -14% Pelagics Other Flying Birds -46% Other Pelagics -52% M-29% Pelagics -68% M-34% M-12% M-36% -55% M-42% -30% M-36% -58% M-41% M-22% +70% M-37% +176%

Myctophids Myctophids +3% Myctophids

Adult Adult Krill Adult Krill Krill -92% -98% -75% Molluscs Crustaceans -88% Crustaceans -19% Copepods Crustaceans Molluscs -27% Copepods Molluscs -92% +13% Copepods +193% -14% -62% +45% (a) S=1.35, E=0.91 (b) S=1.35, E=0.83 (c) S=1.35, E=0.96

Figure 5.7: Changes in biomass for myctophids with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100% 146 H2H2b Deep Demersals Large H2H2a Deep Demersals Large L1H1 Deep Demersals Large Chinstrap Chinstrap Chinstrap Penguins Penguins Penguins Gentoo Gentoo Gentoo Penguins Other Other Other 72% Penguins Penguins Icesh 75% Icesh 84% Icesh 69% M59% 47% 69% M57% 79% M68% M55% M48% 58% 66% M60% M35% M47% M51% Deep Deep Deep Demersals Demersals Demersals Lg. Lg. Lg. 9% 18% 42% Adult Adult Adult Krill Krill Krill 93% 98% 75% Molluscs Molluscs Molluscs 92% 89% 62% Juvenile Crustaceans Juvenile Juvenile Crustaceans Krill Krill Crustaceans Krill 19% 94% 91% 77% 27% +13% (a) S=0.65, E=0.46 (b) S=0.65, E=0.41 (c) S=0.65, E=0.48

Figure 5.8: Changes in biomass for large deep demersals with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100% 147 L1H1 Toothsh H2H2a Toothsh H2H2b Toothsh

Cephalopods Cephalopods S. Elephant S. Elephant Cephalopods S. Elephant 41% Seals Seals 46% 14% Catches Seals Catches Catches 65% M13% 100%* 78% M43% 100%* 82% M34% 100%* M60% M67% M67% F100% F100% F100% Toothsh Toothsh Toothsh 55% 56% 37%

N. gibberifrons N. gibberifrons N. gibberifrons 26% Other 38% Other 22% Other Icesh Icesh Icesh 58% 47% 66% Cephalopods Cephalopods Small Small Small Nototheniidae Cephalopods Nototheniidae 46% Nototheniidae 41% 14% 35% 18% +10%

(a) S=0.16,E=0.12 (b) S=0.16,E=0.11 (c) S=0.16,E=0.13

Figure 5.9: Changes in biomass for toothﬁsh with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass, mortality, and catches are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100%. *Indicates ending biomass is reduced by 100%. 148 5.4. Results

Other Fish

The ’other pelagic’ group, which is ecologically similar to the myctophid group, shows declines in biomass for future scenarios. Compared to the myctophid group, both groups have lower mortalities in the future due to declines in predators. However, diets of myctophids show a greater contribution to prey items favored under future scenarios; other phytoplankton, copepods and macro-zooplankton, while the other pelagic group has a higher contribution in the diets of declining species such as krill and cephalopods. The small deep demersal group follows similar biomass patterns as the large deep demersal group, although increases are more extreme. Future mortality for this group is also reduced, but increases are driven by larger contributions to the diet for macro-zooplankton and crustaceans groups, both of which are increasing. The large notothenoiidae, small notothenoiidae, N. gibberifrons, shallow demersals, and other icefish groups all show declines in biomass and mortality. Increased contributions of other phytoplankton, crustaceans and macro-zooplankton in these diets are identified for these groups, however initial diets in the Ecopath model contained moderate contributions from krill and crustacean groups. C. gunnari biomass declines while mortality increases in future scenarios. The starting diet of this group is also heavily weighted on krill and benthic invertebrates, most of which decline in the future. However, there is some top-down pressure from increased mortality from penguins, seals and other fish groups. P. atarcticum biomass and mortality both decline in future scenarios with the exception of the H2b harvest scenarios. As there are increases in contributions to copepods and crustaceans in the diet of P. atarcticum, biomass increases as these prey items also increase in these scenarios.

149 5.4. Results

Birds

Adelie Penguins

Adelie penguins decrease due to bottom up changes in the food web. For the L1H1 scenario biomass is the highest of any other future states at a decrease of 75%, while the greatest decrease in biomass is observed for the H2H2b scenario at a 98% decrease (ﬁgure 5.10). Total mortality increases under this scenario, as the contribution of Adelie penguins to the diets of leopard seals increases. This is likely a result of other, smaller populations of penguins being less available to leopard seals, as they are declining due to diminishing food resources as well. Even under the L1H1 scenario, predation mortality of leopard seals increases by 123%, however total mortality only increases roughly 10% (from 0.29 to 0.31). It is likely that if enough resources were available, the Adelie penguin population could withstand a 10% increase in predation.

Other birds

Macaroni penguin biomass declines in future scenarios coupled with increased mortality, similar to the Adelie penguins. Predators of macaroni penguins also include killer whales and leopard seals, meaning increased mortality is caused by these groups. Flying birds along with emperor, gentoo and chinstrap penguins have quite large variances in biomass for the L2H2b scenario. While results for fish groups with similar patterns were attributed to large variations in copepods and macro-zooplankton, diets of penguins are more heavily weighted on cephalopods which also show this trend. However, macro-zooplankton are a key prey item for cephalopods, so the increased variation is in part due to the macro-zooplankton group. Biomasses for all of these groups decline in conjunction with lower mortalities in the future. Also worth noting in the increase in salp contribution to the diets of flying birds, chinstrap and gentoo penguins. Although the contribution to the diet was low in the Ecopath model (2% for chinstrap and gentoo groups and 5% for flying birds), it becomes the predominant

150 5.4. Results contributor to diets in the future due to increased biomass in the model.

151 L1H1 Adelie Penguins H2H2a Adelie Penguins H2H2b Adelie Penguins Leopard Leopard Leopard Seal Seal Killer Seal Flying Killer -94% Flying Whale -80% -91% Birds Whale Birds -96% Killer -94% M+123% Flying M-31% Whale -62% M-93% -68% M+54% Birds M-93 % -92% M-36% M-52% -52% Adelie M-88% M-8% Adelie Penguins Adelie Penguins Penguins -94% -75% -98% Adult Krill Adult Krill Adult Krill -92% Crustaceans Crustaceans -98% Crustaceans -75% Molluscs -27% Molluscs Molluscs -19% +13% Juvenile Krill Juvenile Krill -92% -88% -62% Juvenile Krill -90% -94% -77%

(a) S=0.29, E=0.31 (b) S=0.29, E=0.26 (c) S=0.29, E=0.41

Figure 5.10: Changes in biomass for Adelie penguins with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100% 152 5.4. Results

Marine Mammals

Minke Whales

Changes to minke whale biomass are driven from bottom-up changes in the ecosystem. Killer whales are the only predator, and biomass of this group declines over 90% from the starting biomass for all scenarios with predation mortality decreasing 90%, 87% and 88% for the L1H1, H2H2a and H2H2b scenarios respectively. If food resources were suﬃcient, release from predation mortality should cause an increase in whale abundance. However, there are large declines in the main prey species of minke whales. Of the four main prey items; adult krill, juvenile krill, copepods and micro-zooplankton, copepods are the only group to increase and only in select scenarios. For the L1H1 scenario, minke whale declines are caused by bottom-up factors (prey), as all main prey items are decreasing. In the H2H2a scenario minke whales decline slightly less (1%), even though copepod biomass shows an overall increase. This is not enough to compensate for the further reduction of krill. In the H2H2b scenario minke whale biomass is highest at a decline of only 23% from the starting value. Macro-zooplankton, a smaller contributor to the diet shows increases in biomass for the High climate scenarios, speciﬁcally the H2H2b scenario. Shifts from declining krill to increasing copepods and macro-zooplankton in the diet result in a less severe decline in this scenario.

Antarctic Fur Seals

Antarctic fur seals show large declines across all scenarios (figure 5.2). For the L1H1 scenario, declines are caused by declines in prey (figure 5.12). The two predators of fur seals; killer whales and leopard seals both decline themselves, as does the predation mortality caused by these groups. Even in scenarios where killer whale biomass is highest, but still lower than starting values, this is in combination with lower biomass values of leopard seals in these scenarios (figure 5.13). The largest contributors to the diet of fur seals; adult krill, juvenile krill, cephalopods and N. gibberifrons all decrease in all

153 5.4. Results three scenario compared. While there are some diﬀerences in the degree of prey item declines, it appears as though reductions of prey items in the model are severe enough to cause declines of fur seals >90% for each of the scenarios presented.

154 L1H1 Minke Whales H2H2a Minke Whales H2H2b Minke Whales Killer Killer Whale Killer M-90% Whale Whale -94% M-87% -96% -92% M-88% Minke Minke Minke Whales Whales Whales -45% -44% -23%

Copepods Copepods Copepods -14% Micro- Micro- Micro- Zooplankton Zooplankton -77% Zooplankton -100%* Juvenile Krill -100%* Juvenile Krill -100%* -75% +45% Adult Krill Juvenile Krill -90% +193% Adult Krill -94% Adult Krill -92% -98%

(a) S=0.064, E=0.042 (b) S=0.064, E=0.041 (c) S=0.064, E=0.038

Figure 5.11: Changes in biomass for minke whales with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100%. *Indicates ending biomass is reduced by 100%. 155 H2H2b Antarctic Fur Seals L1H1 Antarctic Fur Seals H2H2a Antarctic Fur Seals

Killer Leopard Killer Leopard Whale Seal Whale Seal Killer Leopard -94% M-90% -96% M-87% -94% Whale Seal -80% M-86% -92% -91% M-16% M-69% A. Fur A. Fur A. Fur M-71% Seals Seals -91% Seals -94% -92%

Adult Krill Adult Krill Cepalopods Adult Krill -92% Cepalopods Cepalopods -98% -14% Notothenia -46% -75% Gibberifrons Notothenia Notothenia -49% Gibberifrons Juvenile Krill -26% Juvenile Krill Gibberifrons Juvenile Krill -22% -90% -38% -94% -77%

(a) S=0.175, E=0.172 (b) S=0.175, E=0.119 (c) S=0.175, E=0.119

Figure 5.12: Changes in biomass for Antarctic fur seals with important contributors to diet and mortality. S (mortality at the start of the model 1978) is presented along with E (mortality at the end of the scenario simulation). Ending values for biomass and mortality are averaged over the last 10 years of the simulation. Open circles represent initial biomass for the Ecopath model (1978 value), while the shaded circles represent the ending biomass and are scaled to represent the percent change in biomass from the starting biomass of 100% 156 5.4. Results

Other Marine Mammals

Other baleen whales in the model (humpback, blue and fin whales) respond similarly to minke whales for future scenarios. Biomass and mortality decrease (with the exception of fin whale mortality in a few scenarios). Diets that were initially heavily weighted by krill, roughly 70% of the total diets, are replaced by copepods and macro-zooplankton. In most future scenarios copepods contribute over 70% of the diet with macro-zooplankton as the second most abundant prey item for baleen whales. While baleen whales decline in all future scenarios, the H2b harvest scenario results in the least decline for these groups. This can be attributed to the high variability of copepods and the impacts copepods are having on these groups. Crabeater seals, whose diet is heavily based on krill (85% for the Ecopath model), show declines due to a combination of decreased krill in addition to increased mortality. The increased mortality is due to the high starting biomass of this group (relative to other seal species) so as other species decline due to di- minished prey availability, there is increased pressure on crabeater seals by leopard seals and killer whales. Both Weddell and Ross seals show patterns similar to the Antarctic fur seal, whereby biomass declines in all scenarios except the L2H2b scenario. Both Weddell and Ross seals have diets based on cephalopods and fish among other items. Sperm whales and southern elephant seals both have diets based primarily on cephalopods. Declines in both of these groups are greatly impacted by the biomass of the cephalopod group. As top predators, killer whales and leopard seals biomass declines as a reflection of prey items. For killer whales the main prey item, minke whales, continue to be the largest contributor to the diet, even as minke whales decline. Adelie penguins remain the largest contributor to leopard seals diet, although biomass of this group is declining.

157 5.5. Discussion

5.5 Discussion

Changes in Biomass and Trophic Level

Polar ecosystems are expected to exhibit a net decrease in productivity due to climate change, as reductions in the frequency and magnitude of phytoplankton blooms have been attributed to changes in climate over the last 30 years (Montes-Hugo et al., 2009; Schofield et al., 2010). In addition, Antarc- tic sea ice losses are estimated to range from 17-31% depending on climate models and CO2 level increases (Rind et al., 1997; Arrigo and Thomas, 2004). These two factors are responsible for most changes observed in the model. The change in phytoplankton community structure from colder water species (diatoms and ice algae) to warmer water species (cryptophytes and other phytoplankton) through loss of sea ice and increase in SST is responsible for shifting the pathways of nutrients from krill to salps as described by Moline et al. (2000, 2004). There are compounding effects caused by loss of sea ice through mediation functions. The reduction of sea ice increases the vulnerability of larval and juvenile krill to predators while also increasing the foraging area of salps, and further exacerbates the already shifting food web. The implications for krill predators are unfavorable. With this change in producers comes an overall decline in primary production in the model, and a lack of detritus for consumption by benthic organisms, thus reducing the total biomass of the ecosystem. There is a general trend towards increased ecosystem trophic level in the future, as total production and ecosystem biomass decline. Trophic levels of individual species are generally increasing, as diets are shifting away from lower trophic levels such as producers, to higher trophic levels13. Compar- isons of pelagic food webs identified species feed at a lower trophic levels in highly productive ecosystems, typically associated with upwelling, while lower production areas showed species feeding at higher trophic levels (Miller et al., 2011). Although Miller et al. (2011) compared more temperate ma-

13It should be noted that there were no changes to diet composition from the initial Ecopath model, but rather Ecosim simulations allow predators to feed on a variety of species in simulations as their relative abundance shifts.

158 5.5. Discussion rine ecosystems (Japan and California), it was thought the lower TL of the productive ecosystem was due to the increased contribution of zooplankton, primarily euphausiids. TL of the ecosystem increases due to more specialized diets in this study. Comparisons with the Antarctic would ﬁt with the increase in TL of the ecosystem as production decreases and there is a loss of krill in predators diets. In addition, loss of species reduces the omnivory of diets, as proposed by Miller et al. (2011).

Model Uncertainty

Two main factors should be considered with respect to model output. First, the model does not account for changes in physiological limits in species which may cause stress-induced mortality or migration out of the area. Sim- ulations from Cheung et al. (2008) identify distribution shifts of ﬁsh species in relation to climate change. D. mawsoni distribution becomes restricted in Low climate scenarios, and has the potential to become extinct within 30 years of their model simulation under a High climate scenario due to spatial thermal tolerance14. Thermal tolerance ranges of species within the model are not accounted for, and will likely cause additional stress for species such as D. mawsoni. Second, immigration of temperate species into Antarctic waters will increase as these thermal ranges shift. Pelagic species are expected to shift their summer and winter ranges due to thermal tolerances (Lam et al., 2008). Climate change is expected to increase the prevalence of invasive alien species (Dukes and Mooney, 1999) which can alter the food web in ways the existing model does not account for. While barriers to the Southern Ocean are more physiological than geographic, increased warming will shift these physiological barriers pole-wards and allow invasions (Aronson et al., 2007). An important thermal barrier for the Antarctic is the Polar front which sepa- rates the colder polar water from the warmer temperate water. Movement of this thermal barrier further south would allow for increased immigration of temperate species and should be considered when interpreting model results.

14Low climate scenario depicted a temperature increase of 0.075◦C·y−1 at high latitudes, while the High scenario depreciated a 0.15◦C · y−1 at high latitudes.

159 5.5. Discussion

There are other impacts of climate change known to eﬀect species that should be considered in the context of individual species results. UV ab- sorbing amino acids are produced in ice algae and act as a built in sunscreen protecting krill from increased UV (Arrigo and Thomas, 2004). The lack of mycosporine-like amino acids or MMAs in krill, will decrease the UV protection of predators as well, potentially increasing mortality. Fish that rely on sea ice for spawning such as P. antarcticum are expected to show decreased resistance to UV, through reductions of MMAs in krill, in addition to suﬀering from loss of ice habitat (Vacchi et al., 2004; Moline et al., 2008).

Changes to Species Groups

Krill declines in the model range from 75-96% for adult and juvenile groups combined. The krill embryo stage demonstrates increases, as in the model this non-feeding stage is not limited by production within the ecosystem. While this is ecologically founded (Marr, 1962; Nicol et al., 1995; Arndt and Swadling, 2006), the implications to the model allow for a higher biomass of the larval krill stage. This increase in lower trophic levels of krill is not long lived, as juvenile krill biomass declines in future scenarios. Declines from 1978-2007 were estimated by the past model (chapter 3) to be 36% from the starting biomass for juvenile and adult groups combined indicating changes in the future will be more extreme for this group. Studies from the southwest Atlantic sector of the Southern Ocean indicate krill density has potentially decreased 80% from 1976 to 2004 (Atkinson et al., 2004; Smetacek and Nicol, 2005). While this compares density from net data and not biomass directly, it veriﬁes that there may have been signiﬁcant changes to krill populations in the past, and future declines of 75% are not extreme in this context. We consider the implications to the remainder of the food web under the possibility of large scale reductions in krill. Salp biomass increased in the past model of 32%, while ending biomass for future scenarios resulted in increases ranging from 14-75% depending on scenarios. Salp density was noted as increasing two fold (up to or over two fold) from 1926-2003 (Atkinson et al., 2004). Although the density increase

160 5.5. Discussion does not equate to the same changes in biomass, large scale increases are recognized in the data and exhibited by the model presented. As biomass of salps in the model increases, so does the contribution of salps to the diets of predators. Salps are identified to be consumed by a variety of birds, fish and invertebrates (see Pakhomov et al., 2002, for a full list of studies where salps are identified in the diets of predators), and their contribution to the food web increases with biomass. This is acceptable in the model, but the low energetic value of salps should be considered when assessing the potential replacement of krill in predators’ diets. Lipid values as % of wet weight for krill (Euphausia superba) range from 2.41-6.33 for males and gravid females, while salps (Salpa thompsoni) values were 0.1, and copepods ranged from 0.7-9 (Clarke, 1980; Donnelly et al., 1994). Although more recent literature has identified salps to have a higher carbon value than previously described (Ikeda and Bruce, 1986; Dubischar et al., 2006) indicating that although they may have higher energetic values, these values are still lower than reported values for krill and copepods. The release of copepods from krill, specifically juvenile krill, allows for large increases in biomass especially under the L2H2b and H2H2b scenarios. The implications also transfer to the macro-zooplankton and cephalopod groups resulting in large variations in biomass for this group as well (figures 5.13-5.15). High variance in environmental drivers does not cause such large variations in ending results for harvest scenarios H1 or H2a. These scenarios may elude to an instability of the model, or potentially the ecosystem when high variations in environmental drivers are coupled with removals of the juvenile krill, and should be interpreted cautiously. It should be noted that the removals of juvenile krill in the H2b quota level scenario is larger than all krill stages removed presently (also the H1 harvest scenario). Removals of juvenile krill stages should be an important consideration for future managers. Model results for myctophids identify this group fares better than others in the future. While this is due to a varied diet in the model, and declines in predators (primarily penguins and birds), other studies support the potential for myctophid success in the future. Moline et al. (2008) noted lanternfish

161 5.5. Discussion

(myctophid) are not as likely as other species to be impacted by loss of sea ice as their life history has little direct dependence to ice. di Prisco and Verde (2006) also suggest the replacement of ice-associated fish by myctophids as a new food item for higher trophic levels. Predictions of the future changes to benthos are difficult (Clarke and Crame, 1992). Aronson et al. (2007) suggests the overall biomass of endemic species may be replaced by benthic invaders such as crabs. This would be possible in the future when physiological limitations would dimin- ish as waters warm, as there are no physical barriers preventing invasions. There is the potential that invasions by sub-Antarctic species of benthos and fish would substitute the diets of predators, thus reducing the impacts of declining krill populations. Declines in Adelie penguins attributed to declines in ice habitat have been established in literature, but it was thought that gentoo and chinstrap penguins were increasing during the same period due to their ability to inhabit warmer areas (Fraser et al., 1992; Fraser, 2006). The model fitted to past data (chapter 3) included immigration rates in an attempt to replicate the Palmer LTER data (Fraser, 2006), but could not account for increases in chinstrap and gentoo penguin biomass. More recent, larger scale studies on penguin populations reveal that these species are declining, with the primary cause being attributed to krill declines (Trivelpiece et al., 2010). Model simulations corroborate the ability of krill declines to explain these declines in all penguin species. There are yet to be any large-scale studies for whales or seal populations to validate model results for these groups. Past studies have indicated declines in predators at S. Georgia due to years of low krill biomass (Hofmann et al., 1998; Brierley and Reid, 1999; Reid and Croxall, 2001), which is the reason for declines in these predators in the model.

Role of Krill in the Ecosystem

Krill are thought to have a pivotal role in Antarctic ecosystems, linking top predators to primary production (Moline et al., 2000, 2004; Smetacek and

162 5.5. Discussion

Nicol, 2005). Baleen whales migrate from tropical latitudes to feed on krill in summer months (Dawbin, 1966; Tynan, 1998; Murase et al., 2002) indicating their life history is based on krill being available every summer. Model results indicate the potential of krill to be replaced in part by copepods in the ecosystem. Chapter 3 suggested krill may be over-represented in the diets of predators in literature. It is possible within the model to substitute the diets of krill predators with copepods, as predators generally consume both. Energetically, copepods represent a nutritional equivalent to krill with higher lipid concentrations in some cases when comparing gram for gram (Clarke, 1980; Donnelly et al., 1994), more so than salps which also increase in the future. It is unclear whether conditions in the future will actually favor the increase in copepod species, however it may be a consideration for future research.

Future Harvest of Krill

Comparing harvest scenarios, increasing the krill harvest from present day levels to current quota levels further decreases krill biomass in simulations. When comparing the H2a scenario where only adult krill are harvested to the H2b scenario where 25% of the catches are taken from juveniles, there are much larger reductions in biomass for adult and juvenile krill. There are fewer adults being harvested in the H2b scenario, but the targeting of juvenile krill reduces the availability of krill to reach sexual maturity. While these levels were tested based on length-frequency catches of krill and length at maturity (Siegel and Loeb, 1994; Pakhomov, 1995a; Jackowski, 2002), the results should be considered to address the issue that bycatch of smaller krill may be altering adult biomass. Further research into removals of juvenile krill should be addressed in management decisions. Overall harvest at quota levels does not in itself identify large scale changes in the ecosystem. Krill declines increase from 76% to 94% from the L1L1 to H2H2b scenarios. However, the model does suggest predators will be strained by a lack of krill in the future. Continued harvest of krill will only enhance the stress. While spatial limitations of the ﬁshery or

163 5.5. Discussion restriction on temporal overlap may allow for some reprieve to predators, managers will have to prioritize between continued harvest and retaining higher abundances of krill predators in the ecosystem. Environmental drivers cause higher declines of krill than increased harvest levels. However, as the model is on a temporal scale, spatial overlap will almost certainly compound these effects. Past declines in predators at South Georgia were attributed not only to declines in krill, but also to fisheries operating in close vicinity to predators (Reid and Croxall, 2001). For the past, spatial modeling of the ecosystem may reveal an increased sensitivity to the krill fishery and cause additional impacts for krill predators. In future simulations, the large scale declines in krill may occur faster with spatial overlap of fisheries, but does not identify this as the cause of the declines. Article II of the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR) addresses the harvest of species (CCAMLR, 1980; Constable et al., 2000). In summary, this article states harvest should be conducted so it: (i) does not decrease any harvested population to levels below those which ensure suitable recruitment, (ii) maintains ecological relationships between harvested, dependent, and related populations, and (iii) prevents changes in the marine ecosystem which are not reversible over two to three decades. Considering the effects to krill by environmental changes alone, managers will have tough decisions to make in the future as expansions of the krill fishery to quota levels will further stress krill populations. Simulations suggest the impacts of the fishery alone may not be great, but it will be taking prey away from higher trophic levels struggling to meet their demands.

164 5.6. Antarctic Peninsula Biomass and Mortality Figures

5.6 Antarctic Peninsula Biomass and Mortality Figures

165 Killer Whales ( 0.001 ) Leopard Seal ( 0.00576 ) Ross Seal ( 0.0042 ) Weddell Seal ( 0.021 ) 0.00020 0.010 0.0015 0.04 0.00015 0.008 0.006 0.03 0.00010 0.0010 0.004 0.02 0.00005 0.0005 0.002 0.01 0.00000 0.0000 0.000 0.00 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Crabeater Seal ( 0.164 ) Ant Fur Seals ( 0.0282 ) S Elephant Seals ( 0.00647 ) Sperm Whales ( 0.005 ) 0.0016 0.004 0.008 0.008 0.0014 0.003 0.006 0.006 0.0012 0.0010 0.002 0.004 0.004 0.0008 0.0006 0.001 0.002 0.002 0.0004 0.000 0.000 0.000 0.0002 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Blue Whales ( 5.000e−04 ) Fin Whales ( 0.003 ) Minke Whales ( 0.065 ) Humpback Whales ( 0.02 ) 0.0020 0.12 0.030 4e−04 0.0015 0.10 0.025 3e−04 0.08 0.020 0.0010 0.06 2e−04 0.015 0.0005 0.04 1e−04 0.02 0.010 0e+00 0.0000 0.00 0.005 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Emperor P ( 0.005 ) Gentoo P ( 0.0065 ) Chinstrap P ( 0.0053 ) Macaroni P ( 0.0135 ) 0.010 0.008 4e−05 0.008 0.006 0.006 3e−05 0.006 0.004 0.004 0.004 2e−05 0.002 0.002 0.002 1e−05 0.000 0.000 0.000 0e+00 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Adelie P ( 0.034 ) Flying birds ( 0.19 ) Cephalopods ( 2.49 ) Other Icefish ( 0.337 ) 0.20 0.010 0.4 0.008 0.15 3 0.3 0.006 0.10 2 0.2 0.004 0.002 0.05 1 0.1 0.000 0.00 0 0.0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b 166

Figure 5.13: Ending biomass by species group for each simulation scenario. Values (in t · km−2) represent the mean and 95% CI for the last 10 years of each simulation. Biomass for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. CI extending into negative values are presented with a minimum biomass of 0t · km−2 Toothfish ( 0.0462 ) Lg Nototheniidae ( 0.59 ) Sm Nototheniidae ( 0.341 ) Shallow Demersals ( 0.0308 ) 0.07 0.8 0.6 0.08 0.06 0.5 0.05 0.06 0.6 0.4 0.04 0.04 0.03 0.4 0.3 0.02 0.2 0.2 0.02 0.01 0.1 0.00 0.0 0.00 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Deep demersals Lg ( 0.042 ) Deep demersals Sm ( 0.08 ) Myctophids ( 0.185 ) Other Pelagics ( 0.49 ) 0.08 0.7 0.20 0.8 0.06 0.6 0.15 0.6 0.5 0.4 0.04 0.10 0.4 0.3 0.02 0.05 0.2 0.2 0.1 0.00 0.0 0.0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b C. gunnari ( 0.29 ) P. antarcticum ( 1.25 ) N. gibberifrons ( 0.81 ) Mollusca ( 9.5 ) 5e−04 4 6 1.0 4e−04 5 3 4 3e−04 0.8 2 0.6 3 2e−04 2 1e−04 1 0.4 1 0e+00 − 0 0.2 0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Salps ( 8 ) Urochordata ( 5.05 ) Porifera ( 12.7 ) Hemichordata ( 0.045 ) 6 7 0.020 20 5 6 5 0.015 4 15 4 0.010 3 3 10 2 2 0.005 1 1 0.000 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Brachiopoda ( 0.0277 ) Bryozoa ( 0.491 ) Cnidaria ( 1.53 ) Crustecea ( 3.61 ) 0.030 0.5 7 0.025 2.0 6 0.4 5 0.020 0.3 4 0.015 1.5 0.2 3 0.010 1.0 2 0.005 0.1 1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b 167

Figure 5.14: Ending biomass by species group for each simulation scenario. Values (in t · km−2) represent the mean and 95% CI for the last 10 years of each simulation. Biomass for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. CI extending into negative values are presented with a minimum biomass of 0t · km−2 Other Arthropod ( 1.01 ) Worms ( 12 ) Echinoidea ( 4.33 ) Crinoidea ( 0.164 ) 7 5 15 0.07 6 0.06 4 5 3 10 0.05 4 0.04 2 3 1 5 2 0.03 0 1 0.02 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Ophiuroidea ( 6.76 ) Asteroidea ( 1.78 ) Holothuroidea ( 5.45 ) Krill Adult ( 9.08 ) 4 2.0 1.0 3 3 0.8 1.5 2 0.6 1.0 2 1 0.4 0.5 1 0 0.2 0.0 0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Krill Juvenile ( 25.3 ) Krill Larvae ( 0.88 ) Krill Embryo ( 0.00631 ) Macro−Zoopl ( 8.17 ) 35 80 30 30 10 25 60 20 40 20 5 15 10 10 5 20 0 0 0 0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Micro−Zoopl ( 2.9 ) Cryptophytes ( 2.2 ) Copepods ( 15.2 ) Diatoms ( 17.4 ) 0.30 120 0.8 3 0.25 100 0.6 0.20 2 80 0.15 60 0.4 0.10 1 40 0.05 20 0.2 0.00 0 0 0.0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Ice algae ( 25 ) Other Phytopl ( 5.5 ) Detritus ( 3.43 ) 14 25 2.5 12 20 2.0 15 10 1.5 10 8 1.0 5 6 0.5 0 4 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b 168

Figure 5.15: Ending biomass by species group for each simulation scenario. Values (in t · km−2) represent the mean and 95% CI for the last 10 years of each simulation. Biomass for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. CI extending into negative values are presented with a minimum biomass of 0t · km−2 Killer Whales ( 0.05 ) Leopard Seal ( 0.12 ) Ross Seal ( 0.13 ) Weddell Seal ( 0.17 ) 0.25 0.07 0.15 0.06 0.15 0.20 0.05 0.10 0.15 0.10 0.04 0.10 0.03 0.05 0.02 0.05 0.05 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Crabeater Seal ( 0.09 ) Ant Fur Seals ( 0.175 ) S Elephant Seals ( 0.165 ) Sperm Whales ( 0.034 ) 0.25 0.20 0.25 0.08 0.18 0.20 0.07 0.16 0.20 0.06 0.14 0.15 0.15 0.05 0.12 0.10 0.04 0.10 0.10 0.03 0.08 0.05 0.05 0.02 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Blue Whales ( 0.0322 ) Fin Whales ( 0.035 ) Minke Whales ( 0.064 ) Humpback Whales ( 0.04 ) 0.07 0.05 0.06 0.030 0.05 0.06 0.025 0.04 0.05 0.03 0.04 0.020 0.03 0.04 0.015 0.02 0.02 0.03 0.01 0.010 0.01 0.02 0.005 0.00 0.00 0.01 0.000 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Emperor P ( 0.15 ) Gentoo P ( 0.22 ) Chinstrap P ( 0.33 ) Macaroni P ( 0.3 ) 0.20 0.30 0.45 0.55 0.40 0.50 0.15 0.25 0.35 0.45 0.20 0.30 0.40 0.10 0.35 0.15 0.25 0.05 0.20 0.30 0.10 0.25 0.15 0.20 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Adelie P ( 0.29 ) Flying birds ( 0.34 ) Cephalopods ( 0.95 ) Other Icefish ( 0.38 ) 0.6 0.40 1.0 0.40 0.5 0.9 0.35 0.35 0.4 0.8 0.30 0.30 0.3 0.7 0.25 0.25 0.2 0.6 0.20 0.5 0.20 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b 169

Figure 5.16: Ending mortality (y−1) by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation. Mortality for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Toothfish ( 0.165 ) Lg Nototheniidae ( 0.37 ) Sm Nototheniidae ( 0.65 ) Shallow Demersals ( 0.75 ) 0.18 0.7 0.75 0.16 0.35 0.6 0.70 0.14 0.5 0.65 0.12 0.30 0.4 0.60 0.10 0.25 0.55 0.3 0.08 0.50 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Deep demersals Lg ( 0.29 ) Deep demersals Sm ( 0.65 ) Myctophids ( 1.35 ) Other Pelagics ( 0.55 ) 1.4 0.7 0.6 0.30 1.2 0.6 0.5 0.25 1.0 0.20 0.5 0.4 0.4 0.8 0.15 0.3 0.3 0.6 0.10 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b C. gunnari ( 0.48 ) P. antarcticum ( 1.1 ) N. gibberifrons ( 0.41 ) Mollusca ( 0.639 ) 1.2 0.45 0.70 5 1.1 0.65 4 1.0 0.40 0.60 3 0.9 0.35 0.55 2 0.8 0.30 0.50 1 0.45 0.7 0.25 0 0.6 0.40 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Salps ( 10 ) Urochordata ( 0.234 ) Porifera ( 0.159 ) Hemichordata ( 0.375 ) 0.26 14 0.24 0.16 0.40 13 0.22 0.14 0.35 12 0.20 0.12 11 0.18 0.30 10 0.16 0.10 0.25 9 0.14 0.08 0.20 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Brachiopoda ( 0.898 ) Bryozoa ( 0.475 ) Cnidaria ( 0.25 ) Crustecea ( 1.05 ) 0.50 1.2 1.0 0.25 0.9 0.45 0.40 1.0 0.8 0.20 0.35 0.8 0.7 0.30 0.15 0.6 0.25 0.6 0.10 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b 170

Figure 5.17: Ending mortality (y−1) by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation. Mortality for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Other Arthropod ( 0.616 ) Worms ( 0.7 ) Echinoidea ( 0.116 ) Crinoidea ( 0.125 ) 0.7 0.14 0.14 0.7 0.6 0.12 0.13 0.6 0.5 0.12 0.5 0.10 0.11 0.4 0.4 0.08 0.10 0.3 0.3 0.06 0.09 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Ophiuroidea ( 0.45 ) Asteroidea ( 0.231 ) Holothuroidea ( 0.316 ) Krill Adult ( 1.5 ) 0.26 6 0.45 0.24 0.35 0.40 0.22 5 0.20 0.30 4 0.35 0.18 0.25 3 0.16 0.30 0.14 0.20 2 0.25 0.12 1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Krill Juvenile ( 0.9 ) Krill Larvae ( 2.5 ) Krill Embryo ( 8 ) Macro−Zoopl ( 7.58 ) 6 6 5 3.5 20 5 4 15 4 3.0 3 10 3 2 2.5 2 1 5 2.0 1 0 0 0 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Micro−Zoopl ( 65 ) Cryptophytes ( 80 ) Copepods ( 26.1 ) Diatoms ( 90.5 ) 160 200 25 80 140 180 20 70 160 15 120 140 60 120 10 50 100 100 5 40 80 80 0 30 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b Ice algae ( 45 ) Other Phytopl ( 105 ) 22 20 100 18 90 16 14 80 12 70 10 60 8 L1H1 L2H1 L1H1 L2H1 H1H1 H2H1 H1H1 H2H1 L1H2a L1H2b L2H2a L2H2b L1H2a L1H2b L2H2a L2H2b H1H2a H1H2b H2H2a H2H2b H1H2a H1H2b H2H2a H2H2b 171

Figure 5.18: Ending mortality (y−1) by species group for each simulation scenario. Values represent the mean and 95% CI for the last 10 years of each simulation. Mortality for the ﬁrst year of simulation is presented above the graph in parentheses for comparison. Chapter 6

Estimating the Economic Value of Narwhal and Beluga Hunts in Hudson Bay, Nunavut

6.1 Synopsis

Hunting of narwhal (Monodon monoceros) and beluga (Delphinapterus leucas) in Hudson Bay is an important activity providing food and income in northern communities, with changes in these species identified in chapters 2 and 4. Despite this importance, there are few studies detailing the economic aspects of these hunts. Here the uses of narwhal and beluga are outlined in addition to estimating revenues, costs and economic use value associated with the hunt, based on harvests for 2007. Incorporation of cost sharing and including an opportunity cost of labour is explored as it influences model outputs. The economic use value for the communities participating in each hunt averaged a negative value of $9399 for beluga and a positive value of $133,278 for narwhal in 2007. When broken down on a per capita basis this yielded mean estimated values of $44 and -$1 for narwhal and beluga respectively. Including the effects of cost sharing with one other hunting activity resulted in increasing the value to $266,504 for beluga and $321,500 for narwhal. Narwhals provide a higher value per whale, in addition to a higher per capita total economic value to the community as resources are shared among fewer communities compared to belugas. However, the beluga

172 6.2. Introduction hunt overall provides greater revenue, as more belugas are harvested. Our results indicate that the value of whales to communities is largely due to their food value, in keeping with literature on other hunting activities in the Arctic.

6.2 Introduction

Subsistence whaling in the Canadian Arctic has been an important activity for native communities, with hunts being culturally significant (Stewart and Lockhart, 2005; Nuttall, 2005). Increasing human populations, combined with declines in marine mammal populations in Hudson Bay, reveal the importance of hunting to this region. Hunting and the use of ’country foods’ (i.e., foods hunted and gathered from the land), are considered an important aspect to life in northern communities, and contribute to reinforcing social and cultural relationships (Nuttall, 2005). Not only does hunting provide a source of protein for people, Inuit have reported a lack of resistance to illness when not consuming country foods (Freeman, 2005). In Hudson Bay (figure 6.1), Inuit culture has been strongly linked to marine species throughout history (Stewart and Lockhart, 2005). They have traditionally hunted a variety of species, including bowhead, beluga, narwhal, polar bears, walrus, seals, fish and birds. The importance of northern species to Arctic communities has been recognized and studied for several years. The state of Alaska had included subsistence hunting as an economic sector in their studies of ecosystem importance (Colt, 2001). The subsistence value of moose in Alaska has also been analyzed (Northern Economics Inc, 2006). Comprehensive assessments of polar bear hunting at various communities in the Canadian Arctic assigned economic values to traditional and sport hunts and includes different perspectives on hunting activities (Freeman and Foote, 2009). Analysis of seal hunting in Canada (Wenzel, 1991) also explored cultural and economic factors involved in hunting. Loring (1996) provided a summary of all summer hunting activities near Igloolik (a community north of Hudson Bay in Nunavut) in 1992, assigning an economic value of $6 million to all hunt-

173 6.2. Introduction ing activities for that year. Research on narwhal and beluga harvest has focused on speciﬁc aspects of individual hunts, such as technical aspects of hunting in general (Weaver and Walker, 1988), and the economic importance of ivory from narwhals (Reeves, 1992a), rather than comprehensive analyses. Unfortunately, such studies are not available for all species or communities involved in hunting. In this paper, we aim to provide an assessment of the economic factors involved in the hunting of two important whale species: beluga and narwhal, in communities in Nunavut, Canada. Community populations in Nunavut (one of Canada’s northern territories) are projected to increase from 32,416 in 2010 to 44,581 in 2036 (Nunavut Bureau of Statistics, 2010), which has the potential to increase pressure on marine mammal stocks in the area. While catches of beluga have remained relatively stable since the 1980s, narwhal catches increased in the late 1990s and remained at a higher level (DFO, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997; Stewart and Lockhart, 2005; JCNB/NAMMCO, 2009). Furthermore, estimates of the 2008 northern Hudson Bay narwhal stock have indicated the possibility of declines up to 50% of previous estimates from 1984 and 2000 (DFO, 2010a), suggesting stock decline although survey results were not conclusive. The 2008 survey noted poor weather conditions, and the population was potentially underestimated in the survey (DFO, 2010a), a 2011 survey is still awaiting results. In December 2010, the Canadian government implemented a ban the export of tusks from the northern Hudson Bay narwhal population, requiring them to remain in Canada, and recommended that CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) implement a ban on the export of these narwhal tusks from communities hunting the northern Hud- son Bay population of narwhals (DFO, 2010b). While this does not aﬀect the quotas on the number of whales that can be harvested, it will ultimately impact the economic value of the narwhal hunt, by limiting the export of narwhal tusks from these communities to within Canada. Communities are currently considering legal options for potential lost revenue. In addition to lost economic potential, the impact of climate change and of harvesting and trade limitations on the culture of northern communities could be largely

174 6.2. Introduction negative (Nuttall, 2005). Of the three beluga stocks hunted within Hudson Bay, the eastern Hud- son Bay population has declined in the past but has not shown a recovery, and has been listed as endangered by COSEWIC (Committee on the Status of Endangered Wildlife in Canada) (NAMMCO, 2005a), while still enduring hunting pressure. Species distributions of narwhal and beluga are expected to contract poleward as a result of climate change, with negative impacts to hunters (Hovelsrud et al., 2008). Hudson Bay has already shown shifts in seasonal ice cover (Gagnon and Gough, 2005), and due to its southerly location will likely be one of the first areas negatively affected by competing species, new predators, disease, and a change in available food. This paper presents the different aspects of the narwhal and beluga subsistence hunts in the Hudson Bay region, along with the economic ramifications to the local communities. While commercial whaling was once important both nationally and regionally, it no longer exists in Canada. Thus, in the context of this paper, hunting and whaling are limited to subsistence hunting. Beluga and narwhal were chosen for the focus of this study as they are hunted annually, and landings are recorded. Analysis was limited to the Nunavut portion of Hudson Bay (figure 6.1), as part of an International Polar Year initiative focusing on Hudson Bay, and have based the model on knowledge of communities within the Nunavut side due to knowledge of the region. For each hunt, revenue, cost and net economic value are estimated for 2007. For the purposes of this paper, use value is presented and refers to economic value hereafter. Our aim is to help facilitate a better understanding of the contributions each hunt brings to the communities discussed, in order to provide baseline information in the event of changes in future hunting patterns. Future geographic expansion of the model for additional Nunavut and Nunavik communities is ideal, but beyond the scope of this project. An Arctic wide assessment acknowledged recent harvesting studies are evaluations on the current knowledge, and are still within the early stages of development (Nuttall, 2005). While this paper aims to be useful in the context of current events, the model presented is a a first step aiming to provide an overview of hunting economics, yet further research is needed

175 6.3. Methods

100°0'0"W 80°0'0"W 60°0'0"W 4-3

NU Repulse Bay

Coral Harbour Chesterfield Inlet 60°0'0"N Whale Cove Rankin Inlet 60°0'0"N Arviat

MB Sanikiluaq QB

50°0'0"N

ON 50°0'0"N

80°0'0"W Figure 6.1: Map of communities in Nunavut portion of Hudson Bay hunting narwhal or beluga. Nunavik, Ontario, and Manitoba communities are not shown. to help develop a more comprehensive economic picture.

6.3 Methods

Using published and unpublished data combined with values provided by ﬁeld researchers, we developed an economic model to estimate the total use value for beluga and narwhal hunts for the 2007 year. For this analysis, total use value was calculated using Monte Carlo simulations whereby parameters for each equation are selected randomly from an assigned distribution to calculate total use value. This is repeated for 10,000 iterations to generate a distribution of values for costs, revenues, total use value and per capita use value. Ranges for input parameters were assigned a uniform distribution to account for uncertainty. Data for catches were taken from 2007, the

176 6.3. Methods most recent year data were published on both narwhal and beluga catches (JCNB/NAMMCO, 2009), with most communities harvesting at or near their quota limit. We present this model as an estimate of economic costs and revenues based on the best available data and assumptions by researcher, but recognize the need for improved estimates in the future, as data sets become richer. Information on hunting activities was taken from published literature where available. Authors provided some estimates, in addition to collabo- rators involved in biological sampling and observation of both the narwhal and beluga hunts in the communities discussed. Speciﬁc prices and costs for individual factors (fuel, meat replacement, narwhal tusk value, carving values) were obtained in 2008 from Repulse Bay. These values were used as representative of costs and prices in other Hudson Bay communities. Carvings (narwhal and beluga) and narwhal tusks are primarily sold to the local Co-op, a locally-owned and democratically-operated northern business that operates as part of a larger network of 31 community-based business enterprizes located throughout Nunavut and the Northwest Terri- tories (Arctic Co-operatives Limited). Each independent Co-op purchases carvings from local artists, and Arctic Co-operatives Limited markets Inuit art on both a retail and wholesale basis, with carvings sold to art dealers, distributers, and the general public (http://www.arcticco-op.com/index.htm). Some hunters may sell carvings or tusks to travelers directly, for a higher price than the Co-op would pay. Prices of tusks and carvings used were based on the value a hunter/carver would receive for selling the piece to the Co-op. Carvings are then generally sold to art dealers, distributers, or the general public resulting in higher prices. A portion of the additional revenue is redirected back into the Co-op, or other community programs. However, as these added values that are generated are not available, values for carvings and tusks was calculated using the price carvers are paid when selling items to the Co-op. Costs were ﬁrst calculated under the assumption that the opportunity cost to hunt was zero. This assumption was then relaxed and costs were re-assessed including an opportunity cost function. Revenues and costs are calculated for the entire hunt and on a per capita

177 6.3. Methods basis. We chose these approaches for two reasons. First, to identify the scope of the use value for both hunts. Second, because in a subsistence economy resources are shared among community members and are an important value of Inuit culture (Wenzel, 2009b,a), we also calculate the per capita use value. All values are presented in Canadian dollars.

Beluga

Belugas begin their annual migration into Hudson Bay in the springtime, from Hudson Strait down the eastern and western coasts of Hudson Bay to their summering locations in eastern Hudson Bay, western Hudson Bay, and James Bay (DFO, 2001). Belugas hunted during these migration routes are utilized for the muktaaq (or thin layer of blubber with the skin attached), with a small portion of the meat consumed or traditionally fed to dogs (Tyrrell, 2007). Communities in Hudson Bay generally do not consume large portions of muscle protein, although other Arctic communities often dry the meat and store for later consumption (J. Orr, pers. comm., 2010). While non-indigenous people may consider this to be wasteful, to Inuit culture a partially-ﬂensed whale is not wasteful. Rather the remaining edible tissues and meat will be consumed by other animals (Freeman, 2005). Teeth and bones, more speciﬁcally, vertebrae, are used for carvings, with bones being dried in the sun for carving at a later date.

Beluga Catch

The 2007 beluga catch, NB, was set to 180 whales for 2007, based on reported catch data (JCNB/NAMMCO, 2009). This included catches from the following communities (number of whales harvested): Arviat (50); Chester- ﬁeld Inlet (12); Coral Harbour (7); Rankin Inlet (38); Repulse Bay (21); Sanikiluaq (52),and Whale Cove (10). Catches for Sanikiluaq were not available for 2007 so a ﬁve year average from 2002-2006 was used as the 2007 catch in these communities.

178 6.3. Methods

Beluga Revenue

The revenue from the beluga hunt is the sum of the value of the meat obtained from the muktaaq, along with other edible portions of the whale, in addition to the income from the vertebrae and teeth, which are turned into carvings. The total revenue of beluga whales, TRB, is calculated for all the whales harvested as:

TRB = RBm + RBc (6.1) where RBm is the value of the meat, for which we essentially use the cost of replacing meat from the beluga whale with store-bought protein sources, and the revenue from beluga carvings, RBc. This replacement value of meat,

RBm, is further broken into:

RBm = NB ∗ wB ∗ eB ∗ cpB (6.2) where wB is the weight of an individual whale, eB is the edible portion of the beluga, and cpB is the replacement cost of other protein sources from the local store. The revenue from beluga carvings is estimated as:

RBc = NB ∗ [(TB ∗ PBt) + (VB ∗ PBv)] (6.3) where TB is the number of teeth per beluga used for carvings, VB is the number of vertebrae per beluga used for carvings, and PBt and PBv are the prices of carvings from one tooth or vertebrae, respectively.

Beluga Cost

The cost of the beluga hunt is calculated for all communities combined (Arviat, Chesterfield Inlet, Rankin Inlet, Repulse Bay, Sanikiluaq, and Whale Cove). Baker Lake was excluded from the model as there was only one year of reported hunts from 1977-2007 (JCNB/NAMMCO, 2009). Costs here include both variable (i.e., bullets and fuel) and fixed costs (i.e., rifles and

179 6.3. Methods boats). Within the ﬁxed cost estimate a term for depreciation is included. Costs are calculated on a per trip basis, with the number of beluga hunting trips, Btrip, dependent on the number of hunters ,MB, the number of trips each individual takes, IB, and the size of the hunting group, Bgr:

MB ∗ IB Btrip = (6.4) Bgr

The per trip costs of the beluga hunt are broken into the cost of boats, CBb, the cost of guns, CBg, the cost of fuel, CBgs, and the cost of bullets, CBbu.

The cost of all boats per trip, CBb, is estimated as:

Nbo ∗ (cbo/Tbo) CBb = (6.5) Btrip where Nbo, the number of boats, is represented as MB/Bgr. The parameter cbo is the cost of one boat, with a replacement time, Tbo. The cost of all guns per beluga trip, CBg, assuming each hunter has 1 gun, is estimated as:

MB ∗ cgu/Tgun CBg = CR ∗ (6.6) Btrip

With cgu as the cost of one gun, Tgu as the replacement time of a gun, and CR as the percentage of hunters participating in the Canadian Ranger program. Canadian Rangers are a component of the Canadian Forces and are responsible for surveillance, patrolling, reporting activities, and collecting data http://www.army.forces.gc.ca/land-terre/cr-rc/crpg-gprc-eng.asp. This program subsidizes hunters by providing guns, which is further explained in Section 2.5. The cost of bullets per trip, CBbu, is estimated as:

bu ∗ MB ∗ IB ∗ cbu CBbu = CR ∗ (6.7) Btrip where the total number of bullets used is dependent on the number of men hunting, MB, the number of trips each hunter takes, IB, the number of bullets used per hunter, bu, and the cost of each bullet, cbu. The cost of fuel

180 6.3. Methods

per beluga hunting trip, CBgs, is estimated as:

CBgs = L ∗ cgs (6.8) where L is the liters of fuel used per trip, and cgs is the cost of fuel per liter.

The total cost for hunting beluga over all trips, TCB, is the sum over the costs for the boat, guns, bullets and fuel:

TCB = Btrips ∗ (CBb + CBg + CBbu + CBgs) (6.9)

Beluga Total Use Value

The total use value from the beluga hunt, ΠB, is calculated as the diﬀerence between the total revenue and total cost:

ΠB = TRB − TCB (6.10)

We also computed this value on a per capita basis, πB, estimating the value of the hunt to every member of the community, based on the population size

Bpop. ΠB πB = (6.11) Bpop

Narwhal

The narwhal hunted in Hudson Bay are part of the northern Hudson Bay stock. Historically, this population was believed to be part of the larger Baf- ﬁn Bay narwhal population. However, recent studies indicate that, although winter ranges have the potential to overlap, Hudson Bay narwhal show summer site ﬁdelity near Southhampton Island and Repulse Bay (COSEWIC, 2004a; Westdal et al., 2010). Narwhal leave their winter range to begin migration to their summer location near Repulse Bay around May and stay until the beginning of September (Westdal et al., 2010). The narwhal hunt in Repulse Bay generally starts after the ice breaks up in mid June, and continues until August when the whales leave the area (Freeman et al., 1998). 2007 was a year of unprecedented decline in sea ice, allowing the quota to be

181 6.3. Methods reached easily as narwhal were able to come close to shore (Cressey, 2007; Greer, 2007). During years of high sea ice, narwhal are hunted from the ice edge using snowmobiles, but after the ice is gone they are hunted from the open water using boats (Weaver and Walker, 1988). Snowmobiles were not included in this analysis for narwhal due to 2007 being a low ice year and boats as the primary tool for harvest of narwhals in Hudson Bay (DFO, 1998; Greer, 2007). Narwhal have traditionally been hunted for muktaaq, which is highly prized in native communities (Hrynyshyn, 2004; Freeman, 2005). In addition, tusks from the male narwhals are sold to the local Co-op, where they are picked up by art dealers to be sold in other locations. While females generally remain functionally toothless throughout life, a small percentage do grow a full length tusk. Additionally, there have been reports of males with two erupted tusks, however it has been estimated that these are rare cases occurring in less than 1% of the population (Reeves, 1992a), and therefore this possibility was not incorporated into the model. Teeth and bones from both males and females are used for carvings and are sold to local tourists, or to the Co-op for further distribution.

Narwhal Catch

Narwhals are typically hunted at 3 Hudson Bay communities: Repulse Bay, Rankin Inlet, and Whale Cove, with most, if not all, of the catches in most recent years from Repulse Bay. For 2007, the total catch, NN , was reported as 81 whales: 9 whales from Rankin Inlet, 72 whales from Repulse Bay, and none from Whale Cove (JCNB/NAMMCO, 2009). As male narwhals have a higher value, due to their tusks, catches were split into male, NM , and female, NF , whales. Of the 72 whales reported from Repulse Bay in 2007, 41 were male (DFO unpublished data). The male catches from Repulse (56%) were assumed to also be representative of Rankin Inlet for 2007.

182 6.3. Methods

Narwhal Revenue

Total revenue of narwhal, TRN , is calculated following the same method as beluga for two separate uses: consumption in the form of narwhal meat, plus the revenue from narwhals in the form of carvings.

TRN = RNm + RF c + RMc (6.12) where RNm is the revenue from narwhal meat (males and females), RF c is the revenue from female carvings, and RMc is the revenue from male carvings, due to diﬀerences in revenue caused by male narwhal tusks. The revenue from the meat is calculated as the replacement cost of protein:

RNm = [(NF ∗ wNF ) + (NM ∗ wNM )] ∗ eN ∗ cpN (6.13)

With wNF and wNM as the weights of female and male narwhal respectively.

The edible portion of narwhals, eN , and cpN is the cost of meat replacement per kg of narwhal meat. The revenue from the female carvings RF c, which is comprised of incisor teeth and vertebrae is equal to:

RF c = NF [(Fto ∗ Pto) + (VN ∗ PNV )] (6.14) where Fto is the number of incisor teeth used for carvings for females, Pto denotes the price of the carvings made from teeth, VN , is the number of vertebrae used per whale, and PNV is the price for a carving made from a vertebrae. Prices of carvings for teeth and vertebrae, in addition to the number of vertebrae used per whale were the same for male narwhals. For males the revenue is split into revenue from vertebrae and teeth, RMvt, and revenue from tusks, RMtu. Revenue from the male vertebrae and teeth was set to:

RMvt = NM ∗ [(Mto ∗ Pto) + (VN ∗ PNV )] (6.15) using the same prices for carvings and teeth as females. Mto is the number of teeth used for carvings from male narwhal.

183 6.3. Methods

Revenue from male tusks is estimated as:

RMtu = NM ∗ [(Tw ∗ Ltu ∗ Pwt) + (Tc ∗ Ltu ∗ Pct)] (6.16) where Tw is the percentage of tusks sold whole with the price of whole tusks,

Ptu, and Tc is the percentage of tusks turned into carvings, set to (1-Tw), with the price of tusk carving, Pct. It should be noted that both prices are dependent on the length of the tusk, Ltu.

Narwhal Cost

The narwhal total cost, TCN , is calculated for the communities of Repulse Bay and Ranklin Inlet, using the same basic equations as the beluga hunt, with the same ranges associated to costs of boats, guns, and bullets, in addition to replacement times. Costs are calculated on a per trip basis, with the number of narwhal hunting trips, Ntrip, estimated as:

MN ∗ IN Ntrip = (6.17) Ngr where MN is the number of narwhal hunters in the two communities, IN is the number of individual trips each hunter takes, and Ngr is the size of narwhal hunting groups.

Costs for individual boats, guns, and bullets, (cbo, cgu, and cbu) used the same values as the beluga hunt. Replacement times for boats and guns,

(Tbo and Tgu) also used the same values. The per trip cost of boats hunting narwhal, CNb, is estimated as:

NNb ∗ (cbo/Tbo) CNb = (6.18) Ntrip with the number of boats for narwhals, NNb, depending on the number of hunters and the size of hunting groups (MN /Ngr). The cost of guns per narwhal trip, CNg, is calculated assuming each hunter has one gun:

MN ∗ cgu/Tgu CNg = CR ∗ (6.19) Ntr

184 6.3. Methods

The cost of bullets per trip:

bu ∗ MN ∗ IN ∗ cbu CNbu = CR ∗ (6.20) Ntrip used the same cost per bullet, cbu, as beluga hunting. Finally the cost of fuel used per narwhal trip, CNgs, was set to:

CNgs = L ∗ cgs (6.21) with the liters of fuel used per trip, L, and cost of fuel per liter, cgs. The total cost of narwhal hunting, TCN , is therefore calculated as the sum of all cost components: boats, guns, bullets and fuel:

TCN = Ntr ∗ (Cnb + CNgu + CNbu + Cngs) (6.22)

Narwhal Total Use Value

The total use value for narwhals, ΠN , is calculated as the diﬀerence between the total revenue and total cost of the hunt:

ΠN = TRN − TCN (6.23) with the per capita value, π, calculated as:

ΠN πN = (6.24) Npop where the population size of narwhal hunting communities, NP op.

Opportunity Cost

In the above cost functions, we assumed that the opportunity cost of labour, essentially what the hunter must forgo in order to hunt, is equal to zero. This assumption was based on anecdotal evidence from other hunts such as polar bear hunting in Clyde River where Inuit commented on working casual employment to cover the costs of hunting supplies before quitting to go hunting

185 6.3. Methods

(Wenzel, 1991). Other researchers have commented on the perception that hunters prefer hunting to alternative employment, even taking vacation time or missing work to hunt (B. Dunn and J. Orr pers. comm 2010). Jobs in some northern communities can be hard to obtain (Loring, 1996). Economic assessments have in the past assigned a wage to hours worked to calculate the opportunity cost of hunting such as Foote and Wenzel (2009) who used an opportunity cost of $12 an hour for polar bear hunting in Clyde River. A sensitivity analysis was performed on the opportunity cost of hunting to identify how our assumption of opportunity cost equal to zero aﬀected total cost and economic use value. Here, we calculate the opportunity cost per community based on the average income and the time spent hunting. To do this, the median income for persons over 15 (In), is multiplied the ratio of the number of employed people in the community (Nem), to the total number of people in each community in the work force (employed and unemployed)

(Nlf ) to give an average income per employable community member. This value was then multiplied by the number of hunters Nhun and number of days spent hunting (Dhun). OC per community is thus calculated as:

Nem OC = In ∗ ∗ Nhun ∗ Dhun (6.25) Nlf Income and employment numbers were obtained from census data of each community (Statistics Canada, 2006). The number of hunters is either

MB or MN for the beluga and narwhal hunt respectively, while the number of days spent hunting each year is equal to the number of trips per hunter

(IB or IN for beluga and narwhal hunts respectively). Opportunity cost for communities hunting both narwhal and beluga are calculated separately for each hunt.

Cost Sharing

Use values for beluga and narwhal hunting activities are calculated under the assumption that all costs are incurred for each hunting activity independently. For example, that hunters purchase a boat and a gun speciﬁcally for the purpose of hunting beluga or narwhal. Rankin Inlet is a community

186 6.3. Methods which hunts both narwhal and beluga, in addition to other species. It is almost certain that, in this community, gear is used for both hunts, therefore reducing the costs associated with each individual hunt. For all communities, the cost of hunting whales is re-assessed with the new assumption that costs are shared with other hunting activities, as boats, guns, and fuel may be used to hunt a variety of species (whales, seals and birds) on the same trip. While there are some trips which are intended to hunt beluga or narwhal exclusively, this was considered a rarety rather than the norm, occurring more frequently for narwhal hunts which have a shorter season than beluga. Given this cost-sharing possibility, we reassessed the cost of hunting according to the number of other hunting activities hunters are likely to participate in throughout the year. Costs for boats, guns, and fuel were shared, however costs for bullets remained the same as they can only be used once.

Model Inputs

Parameter values used for model inputs are summarized in table 6.1. Catch statistics for both hunts were used as single estimates rather than ranges, as data were provided (JCNB/NAMMCO, 2009). Proportions of male vs. female narwhals were provided from catch records through DFO (DFO unpublished data) for Repulse Bay where a majority of narwhal are caught. The same proportion of male to female narwhals was applied to catches from Rankin Inlet.

187 Table 6.1: Parameters inputs for model equation.

Parameter Lower Range Upper Range Description References

NB 180 180 # Beluga (JCNB/NAMMCO, 2009; NAMMCO, 2005a)

wB 600 1,100 Weight of beluga (Kg) (Brodie, 1971) eB 5 25 Edible portion of Beluga (% Body weight) (Reeves, 1992b; Ashley, 2002; Hrynyshyn, 2004; Tyrrell, 2007) 1 cpB 6.9 39 Replacement cost of meat ($ per Kg) † TB 0 2 Teeth per beluga ‡ VB 0 2 Vertebrae per beluga ‡ Pt 20 200 Price of carving for 1 tooth($) † Pv 60 250 Price of carving for 1 vertebrae($) † MB 10 40 # of beluga hunters (% of community) ‡ Bpop 7,364 7,364 Population of all beluga communities (Statistics Canada, 2006) Bgr 1 5 Beluga hunting group size ‡ IB 10 15 Trips per beluga hunter (# trips/year) ‡ cbo 3,000 20,000 Cost of boat ($) ‡ NN 81 81 # Narwhal (JCNB/NAMMCO, 2009) Nf 35 35 # Female Narwhals (DFO unpublished data) Nm 46 46 # Male Narwhals (DFO unpublished data) wNF 800 1,000 Weight of female narwhal (kg) (Garde et al., 2007) wNM 1,500 1,800 Weight of male narwhal (kg) (Garde et al., 2007) eN 5 25 Edible portion of Narwhal (% Body (Reeves, 1992b; Wenzel, 1991; Ashley, weight) 2002; Wenzel, 2009b) 1 cpN 6.9 39 Replacement cost of meat ($ per Kg) † Fto 0 2 Teeth per female Narwhal ‡ Mto 0 1 Teeth per male Narwhal ‡ VN 0 2 Vertebrae per narwhal ‡ 188 Continued on Next Page Table 6.1 Continued Parameter Lower Range Upper Range Description References

Ltu 2.5 8 Length of tusks (feet) (Weaver and Walker, 1988; Garde et al., 2007; Reeves, 1992a)

Tw 95 100 % of tusks sold whole (CITES, 2004) Rwt 100 180 Revenue from whole tusk per foot ($) † Pct 60 200 Price of tusk carving per foot ($) † MN 20 50 # of Narwhal hunters (% of community) (Greer, 2007)‡ Ngr 1 5 Narwhal hunting group size (Greer, 2007; Sloan, 2008)‡ Npop 3,459 3,459 Population of narwhal communities (Statistics Canada, 2006) IN 5 10 Trips per narwhal hunter (# trips/year) ‡ Tbo 4 10 Boat replacement time (years) (Wenzel, 1991)‡ cgu 700 1,200 Cost of gun ($) (www.cabelas.ca) Tgun 2 10 Gun replacement time (years) (Wenzel, 1991)‡ CR 0 45 % of hunters in Canadian Ranger program (DFO unpublished data) bu 1 10 Bullets per hunter (per trip) ‡

cbu 2 3 Cost per bullet ($) † L 20 100 Gas per trip (Liters) †

cgs 0.9 1.1 Cost of gas per trip ($) † †indicates value was obtained by in 2008 from Repulse Bay ‡indicates value was estimated by authors with assistance of northern ﬁeld researchers (Jack Orr and Blair Dunn) 1 Other studies (Reeves, 1992b; Northern Economics Inc, 2006; Foote and Wenzel, 2009; Wenzel, 2009b) were considered along with collected values 189 6.3. Methods

Composition of body weight for narwhal has been noted as 30-35% of body weight as blubber, 25% muscle, and 10% skin (Reeves and Tracey, 1980). A summary of edible weights from the 1960s to the early 1980s (Ashley, 2002) indicate upper limits of 45% of body weight for beluga as muktaaq with some muscle, and 37% upper limit for narwhal muktaaq and some muscle. Reeves (1992b) listed multiple sources and values of utilization ranging from 6.9-45.7% of body weight for narwhals and 14-76% for belugas, although it was noted these ranges were higher than observed values within the same paper. Using the average weight for a narwhal (Heide-Jorgensen, 2002), with the amount of muktaaq taken from harvested whales (Wenzel, 1991, 2009b) yields values of 5.9% and 7.8% of the body weight utilized as muktaaq. For belugas a trade between Nunavut and Nunavik of 2,268 kg of muktaaq from roughly 20-30 whales as noted by Tyrrell (2007) would yield 10.5- 15.6% of body weight for an average sized beluga of 725kg (DFO, 2002a; NAMMCO, 2005a). More recent research on belugas estimate lower ranges from 8-10% of body weight consumed (Hrynyshyn, 2004). Estimates from ﬁeld researchers were much lower at 5-12% of body weight being consumed as muktaaq or muscle (Jack Orr pers. comm., 2010). Taking into account the possible exaggeration for the upper ranges on the edible weights for both narwhal and belugas from early studies, the edible portion for belugas eB and narwhals eN were both set to the range of 5-25% of the body weight to include muktaaq and some muscle.

The cost of meat replacement per kg of meat for narwhal and beluga, cpN and cpB, was set to the next best alternative protein source based on values of a variety of meat products (eg., chicken, steak, and ground beef). The replacement cost of meat has been calculated for other hunting activities in Canada and Alaska in the past. Replacement values for other harvested animals have ranged from $8.8 per kg for moose in Alaska for 2005 (Northern Economics Inc, 2006), $8.50-$10.00 per kg replacement of polar bear meat from the 1980s and 2002 (Foote and Wenzel, 2009; Wenzel, 2009b). The lower estimates of replacement value for polar bears were for communities using the meat as dog food, therefore this reﬂects the cost of dog food. In

190 6.3. Methods

1990 narwhal and beluga muktaaq sold through the country-food store in Iqaluit for $17.60-18.99 per kg and $15.40 per kg respectively as they were imported from other communities, although prices are expected to have increased since then (Reeves, 1992b). Our replacement values are higher, based on a variety of chicken, beef, pork and seafood both fresh and canned. While beluga and narwhal meat may be used as dog food, our replacement values consider meat substitutes regardless of their use for human or dog consumption. Both replacement costs of narwhal and beluga, cpN and cpB, were set to the range of $6.90-$39.00 per kg, based on the cost of various protein sources collected from the local Co-op in Repulse Bay in 2008.

For beluga carvings, the number of teeth per beluga, TB, used for carving was set from 0-2 per whale, as younger belugas caught have smaller teeth not generally used for carvings, and older whales can have prominent wear patterns in their teeth. Older teeth are generally unsuitable for carvings, meaning teeth are only extracted from certain whales. In general only larger vertebrae are used for carvings. Teeth and vertebrae are either collected as they are found from previous hunts or are left in the sun to bleach for years before being used as a carving (Jack Orr pers. comm., 2010). The amount of vertebrae per beluga, VB, used for carving was set between 0 and 2, as many hunters do not collect the vertebrae, and not all vertebrae are suitable for carving. Narwhal incisor teeth are used for carving along with narwhal vertebrae. In male narwhals the upper left incisor erupts into a tusk which can be sold whole or used for carving. Female narwhals have 2 incisor teeth,

Fto, thus 2 teeth available for carvings, with the model range set between

0-2. Males have 1 incisor tooth (after the tusk erupts), therefore Mto was set to a range of 0-1. Vertebrae taken from narwhal, VN , was also believed to be low, and was set from 0-2 vertebrae per whale, based on the same reasoning as belugas. The distribution of vertebrae and teeth remained uniform, although only discrete values were used for sampling (values of 0, 1, or 2 only). Teeth and vertebrae are used on their own to make small carvings, or as part of a more elaborate carving which can include parts from various mediums from a variety of animals. The price of one carved tooth for beluga or narwhal, Pt, can range from $20-$60 as part of an earring set or

191 6.3. Methods up to $200 if it contributes to a more elaborate carving. Price of vertebrae carvings, Pv, were set to the range of $60-250, depending on the quality and size of the carving. For males additional revenue is generated from tusks, and is dependent on the length of the tusk. Measurements of narwhals harvested in Pond Inlet from 1982-1983 show a tusk range from 136 to 236 cm (4.46 to 7.74 feet) (Weaver and Walker, 1988). Maximum lengths up to 202 cm have been reported in Greenland (Garde et al., 2007), with rare cases of tusks reported longer than 243 cm (8 feet) (Reeves, 1992a). The range for tusk length, Ltu, was set from 2.5 to 8 feet in the model. Tusks are either sold whole or used for carvings. Recent reports estimate the ratio of whole tusk sales to tusk carvings is high, based on exporting records (CITES, 2004), suggesting relatively few tusks are used for carvings. Therefore Tw, the percentage of tusks sold whole was set from 95-100%, with the remaining 0-5% used as carvings. The price for a whole tusks is the amount a hunter would receive if the tusk is sold to the local Co-op store. In 2008, Repulse Bay hunters were paid $100 per foot for tusks up to 6 feet, and then $15 per inch for every additional inch, this was used for prices of whole tusks, Rwt, in our model. Tusks that are turned into carvings are estimated to generate prices,

Pct, ranging from $60-200 per foot depending on the size and quality of the carving. Costs for each hunt are dependent on the number of hunters participating in each hunt. Based on 2006 census data (Statistics Canada, 2006), there were 2,310 aboriginal men over the age of 15 in the beluga hunting communities, and 1,150 aboriginal men over the age of 15 in narwhal hunting communities. Of these men it was assumed 10-40% of the ones in beluga hunting communities hunt belugas, MB, and 20-50% of these men in narwhal hunting communities hunt narwhals, MN . Women are generally not part of the hunt, although they do help with processing and are considered an important component of the overall activity, the number of men in each community was used as an indicator of the number of hunters. This is not to imply women do not participate or are unimportant to hunting in general, but rather the number of men was used to provide an estimate

192 6.3. Methods as to the number of people participating in each hunt. In Repulse Bay, the hunting season for narwhal is shorter than for belugas in other communities. In 2007 speciﬁcally, the narwhal quota was reached before the end of the season, making it a successful hunt, with a large community involvement (Greer, 2007). Because of the short hunt season and high demand for narwhal, there is a higher proportion of participants for narwhal hunting set in the model, MN . The estimated number of trips taken by each hunter per year was set to 10-15 for belugas, IB, and 5-10 for narwhals, IN , as the hunting season for narwhals is shorter, as the quota tends be reached quite quickly in the hunting season. Group sizes of hunting trips were observed to be between 2-4 for the 2007 narwhal hunt in Repulse Bay (Greer, 2007), however for the model the range was extended to 1-5 hunters for both hunts,

Bgr and Ngr. Gear costs were set to the same ranges for both hunts. There are a range of guns used according to the hunting records from narwhal, with the most common caliber guns in order of frequency of use for hunting; .303, .338, .375, 6.5mm, .308, and the least common 458 (DFO unpublished data). The same gun types and proportions were assumed for beluga hunting. The cost of each gun, cgu, ranges from roughly $700-$1200 as based on prices for .338 and .308 caliber rifles from Cabelas Canada, where a number of hunters purchase their guns (www.cabelas.ca). The .303 caliber rifles used for hunting are provided by the Canadian Ranger program. Community members, including hunters, can enroll in the Canadian Ranger program to assist the Canadian Forces in protecting there communities if necessary, and in return they receive a .303 caliber rifle and 200 rounds of ammunition each year in addition to clothing. Therefore, the cost of these rifles, 55% of the guns used to hunt narwhal in 2007 (DFO unpublished data), are not fully incurred by the hunters themselves, rather the guns are earned by participating in the

Canadian Ranger program. The cost of all guns in the model, (CR), was set from 0-45% of the total value to account for the proportion of hunters belonging to the Canadian Ranger program, and thus receiving .303 riﬂes from the program. Wenzel (1991) noted 4.3 years as a replacement time for guns used in polar bear hunts, with boats and boat motors lasting longer with

193 6.3. Methods replacement times of 6.9 and 4.7 years respectively. We assumed a range of replacement times for guns, Tgun, from 2-10 years within the model. For boats used in the hunts the cost, cbo, was set from $3,000-$20,000 (J.Orr pers. comm 2010) with a replacement time, Tbo, from 4-10 years. Community population size was taken from the 2006 Canadian census (Statistics Canada, 2006). For all beluga hunting communities there were

7,364 people, Bpop, and for narwhal hunting communities, there were 3459 people, NP op.

194 Table 6.2: Community statistics as provided by Statistics Canada Statistics Canada (2006) for Hudson Bay hunting communities

Community Median Income ($) # people employed # people in labor Force # men 15 and over Arviat 15,200 535 615 600 Chesterﬁeld Inlet 21,184 140 160 105 Coral Harbour 14,029 250 310 215 Rankin Inlet 26,389 1010 1125 805 Repulse Bay 10,912 180 275 250 Sanikiluaq 14,368 205 250 240 Whale Cove 16,352 90 100 95 195 6.4. Results

6.4 Results

Beluga

The total revenue from the beluga hunt ranged from $57,667 to $1,995,473 with a mean value of $601,154. Of the total revenue, carvings from teeth and bones contributed an average of $50,156, and meat contributed an average of $550,997 identifying meat as a major contributor to beluga value. The total cost of this hunt ranged from $52,090 to $3,763,073 with a mean value of $593,949, with boats having the highest cost per trip, followed by fuel, guns, and then bullets (ﬁgure 6.2). Economic value for beluga ranged from -$3,709,037 to $1,915,904, with the mean value of -$9,399. The per capita economic value ranged from -$503 to $220 with a mean value of -$1. The opportunity cost of beluga hunting ranged from $217,973 to $718,212, with a mean value of $445,514. When incorporating the opportunity cost into the total cost estimate, the mean total economic value decreases to- $454,859 with the range -$4,210,558 to $1,407,560. Inclusion of opportunity cost decreases the per capita value of beluga hunting to -$61. When cost sharing from other hunting activities is incorporated into the model without opportunity cost, the mean economic value of the hunt increases from $266,504 for cost sharing with one other hunting activity (2 hunting activities altogether), to $487,184 for cost sharing with 9 other hunting activities. The mean per capita economic value increased from $36 to $69 when costs were shared with 1 to 9 other hunting activities (ﬁgure 6.3). However, inclusion of opportunity cost causes a decrease to the per capita economic value which now ranges from -$24 to $5 for cost sharing with 1 and 9 other hunting activities respectively.

Narwhal

The total revenue for the narwhal hunt ranged from $81,267 to $1,413,947, with a mean value of $529,928. Average revenue from meat was $366,100, with tooth and vertebrae carvings from female narwhal generating an average of $9,339, and tusks, teeth, and vertebrae from the male narwhals valued

196 6.4. Results Frequency Frequency 0 500 1000 1500 0 500 1000 1500

0 5 10 15 20 0 5 10 15 20 A: Total Revenue Belugas ($100,000 CAN) B: Total Revenue Narwhal ($100,000 CAN) Frequency Frequency 0 500 1000 1500 2000 0 500 1000 1500 2000

0 5 10 15 20 0 5 10 15 20 C: Total Cost Beluga ($100,000 CAN) D: Total Cost Narwhal ($100,000 CAN) Frequency Frequency 0 500 1000 1500 0 500 1000 1500

−20 −10 0 10 20 −20 −10 0 10 20 E: Total Use Value Beluga ($100,000 CAN) F: Total Use Value Narwhal ($100,000 CAN) Frequency Frequency 0 500 1000 1500 0 500 1000 1500

−30 −20 −10 0 10 20 30 −30 −20 −10 0 10 20 30 G: Total Use Value Including Opportunity Cost Beluga ($100,000 CAN) H: Total Use Value Including Opportunity Cost Narwhal ($100,000 CAN) Figure 6.2: Distributions and 95% CI for Total Revenue (TR), Total Cost (TC), Total Use Value, and Total Use Value including Opportunity Cost. All values are presented in Canadian Dollars

197 6.4. Results

A A

Average Beluga Use Value per capita ($CAN) Beluga Use Value Average B B Average Narwhal Use Value per capita ($CAN) Narwhal Use Value Average −100 −50 0 50 100 150 −100 −50 0 50 100 150

2 4 6 8 10 2 4 6 8 10

Number of hunting activities Number of hunting activities

Figure 6.3: Average per capita use value for beluga and narwhal hunts under (A) cost sharing with other hunting activities, and (B) cost sharing with economic values calculated to include opportunity cost. 2 hunting activities implies either the beluga or narwhal hunt plus one additional hunting activity .

198 6.4. Results at $154,487 on average. The total cost ranged from $58,273 to $2,279,463 with a mean value of $376,821. As in the case of belugas, boats had the highest average cost, followed by fuel, guns, and then bullets. This resulted in the economic value ranging from -$2,120,367 to $1,193,315 with an average value of $133,278. The per capita economic value ranged from -$602 to $348, with a mean value of $44 (ﬁgure 6.2). The opportunity cost of narwhal hunting ranged from $69,763 to $288,113 with a mean value of $160,013. Economic value decreased to a mean value of -$26,735 with a range of -$2,301,919 to $1,025,006 when including opportunity cost, and the per capita values lowered to a mean value of -$7. The economic value and the per capita economic value show increases when costs are shared with other hunting activities. The economic value increases from a mean of $133,278 to $331,500 when costs are shared between 2 hunting activities (narwhal hunting plus one more) up to $472,077 for cost sharing with up to 9 other hunting activities. This leads to an increase in per capita economic value from $44 per person to $96 for 2 hunting activities, and continues increasing to $137 when costs are shared among 10 hunting activities (ﬁgure 6.3). However, with opportunity cost considered, these per capita values decrease to $46 and $90 for cost sharing with 1 and 9 other hunting activities respectively.

Opportunity Cost and Cost Sharing

Table 6.3 identiﬁes the average economic value when costs are shared with other hunting activities, while ﬁgure 6.3 shows the mean per capita values. Although narwhal has a higher value when calculating hunting activities, cost sharing results in beluga hunting having a higher value.

Value Per Community

While all calculations made are based on all communities investing the same costs, and receiving the same revenues, in reality this is not the case. Based on the total revenue and the number of whales landed, the value from each hunt per community was estimated based on a mean revenue of $3,163 per

199 6.4. Results

Table 6.3: Cost sharing results: mean values presented for total economic value of beluga and narwhal PIB and PIN respectively. Economic value is recalculated including the opportunity cost of each hunt.

# Hunting Ac- ΠB ΠB including ΠN ΠN including tivities opportunity opportunity cost cost 1 -9,399 -454,859 133,278 -26,735 2 266,504 -179,009 321,500 161,486 3 358,454 -87,059 384,240 224,227 4 404,429 -41,084 415,611 255,597 5 432,014 -13,499 434,433 274,419 6 450,404 4,890 446,981 286,967 7 463,540 18,025 455,944 295,930 8 473,392 27,877 462,666 302,653 9 481,054 35,540 467,895 307,881 10 487,184 41,670 472,077 312,064 beluga and $6,542 per narwhal (table 6.4). Ignoring costs for a moment, results indicated that Repulse Bay generated the highest revenue with nearly half a million dollars being contributed by narwhal. Not only does this community beneﬁt from a majority of narwhal catches in Hudson Bay, but the added value of hunting belugas identiﬁed a disproportionate amount of revenue being generated in this community compared to other communities.

200 Table 6.4: Contribution of revenue to each community

Community # Belugas Beluga Rev- Opportunity # Narwhal Narwhal Rev- Opportunity Landed enue ($) Cost Beluga Landed enue ($) Cost Narwhal Hunting ($) Hunting ($) Arviat 50 158,150 67,855 – – – Chesterﬁeld In- 12 37,956 16,674 – – – let Coral Harbour 7 22,141 20,924 – – – Rankin Inlet 38 120,194 163,282 9 58,878 136,960 Repulse Bay 21 66,423 15,326 72 471,024 12,803 Sanikiluaq 52 164,476 24,204 – – – Whale Cove 10 31,630 11,917 0 0 10,119 201 6.5. Discussion

6.5 Discussion

In 2007, the total revenue from beluga hunts was higher than that of narwhals, but overall, the narwhal hunt has a higher net economic value. The main reason for this diﬀerence is due to the costs of hunting belugas being higher. As the costs of guns, boats, bullets, and gas were constant between the two hunts, the discrepancy in total costs stems from the number of hunters and the number of trips taken for each of the hunts. Narwhal hunting is more focused compared to belugas, as individual hunters are eager to be part of the community quota before it is ﬁlled. When considering the revenue generated per whale, narwhals are more valuable at $6,542 per whale on average compared to belugas at $3,163. While some of this can be attributed to tusks from male narwhals, the weight of the whale is also important, considering the weight for narwhals used in the model was higher than for belugas. In the case of narwhals, the value of meat (muktaaq and muscle) is higher than that of carvings and tusks. While male narwhals have a higher use value (higher body weight and additional revenue from tusks), the value of meat from narwhals contributes roughly 70% of the total use value of narwhals in this model For these communities, between 50-60% of people over 15 earn an income, with median incomes ranging from from $10,912 in Repulse Bay to $26,389 in Rankin Inlet (Statistics Canada, 2006). Using Repulse Bay as an example, the economic use value (not including cost sharing or opportunity cost) per whale equates to $38 per beluga and $1890 per narwhal. Repulse Bay thus generates $136,878 from hunting whales, as the distribution of catches is not even across communities (table 6.4). Repulse Bay has the lowest median income of all communities at $10,912 with 375 wage-earners, yet the highest value from whaling. The value from whales is the equivalent of 3.3% of the income of each wage earner, meaning each wage earner would have to increase his/her annual income to make up for this loss in the event whaling ceased. The value for other communities would be lower due to a combination of lower contributions of value from whales, as the catches are lower, in addition to higher annual incomes.

202 6.5. Discussion

The per capita use values of $44 and -$1 for narwhal and beluga are lowered when considering time spent hunting (opportunity cost) to -$7 and -$61 per person. Model costs of obtaining and operating gear are high enough to negate the value of meat and crafts derived from the whales. Consid- ering the polar bear hunt in Clyde River, gear costs range from 44-80% of a hunter’s income, with these costs limiting one’s ability to participate in hunting activities (Wenzel, 2009b). Hunters who are employed (wage- earners) are better able to afford and maintain hunting equipment (Wenzel, 1991). Hunting analyses of other species have also identified low economic use values. Economic analysis of seal hunting in Clyde River in the 1980s identified revenues of $1133 per hunter (not per capita), but once costs were considered hunting operated on a deficit (Wenzel, 1991). The subsistence economic value of moose (meat only) was calculated to be $633 per hunter in 2005 (Northern Economics Inc, 2006), again this would be lower if calculated on a per capita basis. One analysis of multiple subsistence hunting activities in Alaska identified an economic value close to zero once opportunity costs were included Colt (2001). The per capita economic values for the narwhal and beluga hunts identify that although revenues may be substantial, considering investments of gear and time, participating in hunting activities is a timely and costly endeavour. There are perceptions that hunting activities in the Canadian north are based on financial desire (Wenzel, 1991), although the model results presented here, and past economic studies indicate there may be other motivations. It has been noted that money is necessary to facilitate hunting activities, rather than being the end goal (Nuttall, 2005). Anthropological literature outlines the cultural importance of hunting activities as well as views on animals as a resource (see Wenzel, 1991; Freeman and Foote, 2009; Schmidt and Dowsley, 2010), although it is not quantified in this analysis. These hunts (and others) most likely have high cultural values driving hunters to participate in hunting activities with low financial returns. One important cultural aspect of the hunt is resource sharing. The concept of sharing food across individuals, families and communities is paramount to the cultural stability in northern communities (Nuttall, 2005). It has been

203 6.5. Discussion reported that this system of sharing is a socially, not economically-based norm (Nuttall, 2005). Despite contributing only a small fraction of the total income to the community, hunting will almost certainly continue to occur due to the cultural and community values associated with these activities. The hunting and sharing (distribution) of country foods, in addition to other resources, is a culturally significant exercise in many northern communities (Nuttall, 2005). It is estimated that 96% of Inuit households share food with the community (Tait, 2001), in addition to the community participation necessary to land and process a whale, and the celebration of the hunt as a core cultural feature to these communities (Freeman, 2005). The value of participating in hunting activities (non-use value) to Inuit provides intangible benefits to hunters and provides a source of identity (Wenzel, 1991; Reeves, 1992b). So while the use value of these hunts is sizable when looking at the hunt as a whole, or on a community basis, the total value to the individual hunter (use and non-use value) is likely much higher than what our current data and model can possibly capture. In this regard, the total value of beluga and narwhal hunts to community members may be underestimated in our study. There are likely other reasons why people would continue to hunt. First, costs could be lower in reality, as previously mentioned, through cost sharing with other hunting activities. Second, subsidies also lower hunting costs, as they are shown to do with fishing (Sumaila and Pauly, 2006; Sumaila et al., 2010). Third, opportunity costs are more likely to be overestimated within the model, rather than underestimated. Continued building on the current model to include additional variables for both costs and revenues will further expand understanding of hunting activities. Future incorporation of additional variables will affect the model in many ways. Revenues from whales outside of food and arts/crafts values include the previously mentioned cultural values, added health benefits and values to scientific research. In the model, muktaaq was assigned a substitute through the next best available protein such as beef, pork, or chicken from the local store. However, in nutritional terms, these may not

204 6.5. Discussion be practical substitutes. Marine mammal blubber and skin contain high levels of retinol (a form of vitamin A), vitamin B, vitamin C, and polyunsaturated fats in addition to being high in protein, while marine mammal muscle is high in iron and zinc Geraci and Smith (1979); Kinlock et al. (1992); Hidiroglou et al. (2008). Diets with higher contributions of country foods and polyunsaturated fats protect against cardiovascular disease with store bought foods having lower values of polyunsaturated fats (Kinlock et al., 1992). The differences in nutritional value between country foods and store bought foods should be considered a limitation of this modeling exercise. Scientific research also benefits from harvested animals. Samples of fat, muscle and other organs are collected by hunters and sent to researchers for analysis. From these samples diet information, health of the whales and genetic analysis can provide valuable data for stock management. Estimates providing the costs associated with hunting will need to be expanded for more precise economic values of both hunts. Information beyond what is presented in the model may alter hunting costs up or down, ultimately affecting the economic value. Additional costs of equipment main- tenance, inclusion of camping gear (stoves, tents, food for multi-day trips) and processing gear (knives, equipment for drying meat) will result in lower economic values than presented in this paper. However, other factors such as cost sharing would lower costs resulting in higher economic values than presented. Although values were presented as though all cost incurred (fixed and variable) were borne solely for the purpose of the individual hunts, this is not representative of hunting in the north. Repulse Bay, for example, participates in both the narwhal and beluga hunt. If hunting activities were combined using the same gear, and narwhal were only hunted opportunistically from ’beluga hunting trips’ then in theory we might assume no costs associated with the narwhal hunt because in this case it would be considered a non-target species. Hunting activities in the north also target a variety of seals, caribou, polar bears, and birds, fish and shellfish. It is almost certain that there is some degree of cost sharing occurring already. Figure 6.3 illustrates the increase in economic value due to cost sharing. Both hunts show an asymptotic shape indicating the greatest increases are happening when

205 6.5. Discussion costs are shared between 2-4 activities, which is likely already occurring in reality. The issue of subsidies has not been fully addressed in the model. We have incorporated the fact that discounts on guns and bullets are offered to some members of the community, as information from harvested narwhals indicates the majority of guns used for hunting (and bullets) were obtained from the Canadian Ranger program. Other subsidies are known to exist for hunters, however the magnitude of the value is not known, nor how these subsidies are filtered down to the hunters. Information regarding numerous programs available through Nunavut Tunngavik Inc. (NTI) aimed to assist Nunavut hunters is available online (http://www.tunngavik.com/programs- and-benefits/frequently-asked-questions/hunters-harvesters/). Various programs under NTI offer subsidies, such as the Nunavut Harvester Support Program (NHSP). The NHSP allows for discounts or assistance under various criteria. These programs offer hunting gear at a subsidized cost, or money to purchase gear through the local HTO, thereby lowering the costs associated with hunting. Furthermore, since carvings and tusks are generally sold through the local Co-op before they are further distributed at higher prices, the Co-op generates revenue from these sales. While the amount or revenue is unknown, profits generated from the Co-op are re-invested in community programs, thereby adding value to the community through these sales. It is also possible for individual hunters/carvers to sell their products directly to art dealers or travelers generating additional revenue directly. The opportunity cost calculated within the model is possibly an overestimate, however more research would be needed to improve estimates. Hunters may make trips after working hours or on the weekends, when not interfering with work, which would lower the opportunity cost. In addition, members of the community have been known to leave work when whales were known to be in nearby areas, forgoing work for hunting. This would imply hunting activities are more important than earning a wage, thus em- phasizing the cultural value of the hunt. While values in this model are derived from hunting, there is the possibility of generating revenue through other avenues such as whale watching.

206 6.5. Discussion

It was estimated that for 2003 over 13 million people globally participated in whale watching, spending over $1.6 billion USD (Cisneros-Montemayor et al., 2010). Yet the notion that whaling and whale watching cannot coexist must be taken into account. Highest potential revenue from whale watching activities exists in locations where tourism infrastructure already exists (Cisneros-Montemayor and Sumaila, 2010). While many northern communities lack a significant flow of tourists, potential exists for the opening of whale watching endeavors. More research is needed to identify the scope of these possibilities including the potential desire for northern communities to participate. Polar bear hunting activities combine sport based ”trophy hunts” for non-natives along with traditional hunts (Dowsley, 2010) indicating some communities may be willing to participate in multiple activities to generate revenue. Perhaps what is most informative regarding this model is the revenue generated from both hunts averages just over $1.1 mil CAN for the 2007 year, with most of the revenue generated as edible products. While this is considered an underestimate for reasons previously mentioned, the total value pales in comparison to the total value of commercial fisheries within Canada, which was $1.95 billion for 2007 (DFO, 2009b). In the case of the narwhal hunt, it is often implied that hunting activities are driven by potential profits from male narwhal tusks. However, for the communities specified in this model, only 56% of catches (from Repulse Bay) were males indicating they were not the sole targets of the hunt at least for the 2007 season. It would appear due to the relatively total value of these whale hunts, when compared with total Canadian fisheries values and the contribution to local annual income, motivations for hunting are generated from a cultural perspective. If, in the event harvesting of whales is not possible in the future due to biological limitations, the economic ramifications to the communities not only in Hudson Bay, but other areas of Nunavut and Quebec, should be taken into account. The trade ban of narwhal products outside of Canada will have impacts to Hudson Bay communities, yet lost revenue appears negligible compared to costs associated with hunting. As the preliminary

207 6.5. Discussion details of these hunts have been presented here, more research is needed to gain a better understanding of various aspects of these activities in northern communities.

208 Chapter 7

Conclusions

7.1 Chapter 1

Chapter 1 provides the background for the dissertation. This chapter focuses on the history of management and a brief ecological background to each area. Both areas demonstrate a history of resource use, changing throughout time. In addition, sensitivity to climate change highlights the desire to gain a better understanding of these ecosystems. Here the modelling framework is introduced for chapters 2-5. Chapter 6 is necessitated in order to determine the motivations behind harvest in Hudson Bay. Ideally this will aid in management strategies. Main conclusions are presented by geographic area rather than chapter progression in the thesis.

7.2 Hudson Bay

Chapter 2

Chapter 2 highlights the construction of the Ecopath model and simulations recreating past changes in the ecosystem. Perhaps the most important aspect of this research is not the model itself, but the model as a tool to identify gaps in existing data. Limited studies have occurred in the area, as there is not a lot of resource use (outside of subsistence harvest) in the area. The quality of a model is a reflection on the data used to create it, and while there are many studies incorporated into the Hudson Bay ecosystem model, some liberties were taken to fill in gaps in data. Despite this, the Ecopath with Ecosim model frame-work allows for the estimation of parameters through food web interactions, such as fish biomass. Total fish biomass

209 7.2. Hudson Bay was estimated to be 3.42 t · km−2 for 1970, based on diet needs of predators, energy produced by lower trophic levels, and structure of the diet matrix. Re-creation of the past ecosystem identifies changes in fish community composition can be explained through benthic-pelagic decoupling. Declines in the ice algae to benthic pathway caused by losses in sea ice, and increases in pelagic production to zooplankton favor planktivorous fish over benthic feeding fish. This is supported by alterations in bird diets, whereby planktivorous fish has been shown to increase throughout the time span of the model simulation (Gaston et al., 2003). Within the model, lower trophic levels are more heavily influenced by environmental drives used. The effects become dampened to higher trophic levels. However, higher trophic levels are more heavily influenced by harvesting activities. Populations identified within the model to have shown declines related to harvest are; polar bears, narwhal, eastern Hudson Bay belugas and bearded seals. In Hudson Bay there is a need for more research on all ecosystem components, however fish were identified to the the weakest link of the model. Basic surveys of fish in the region would be extremely useful for future research and model validation. As only 2 plankton studies have been conducted (Harvey et al., 1997, 2006) continuation of surveys over time will allow researchers to form a better picture of changes. For example surveys on the Hudson Bay narwhal population were completed in 1984 and 2011 (Richard, 1991, 2010) identifying declines in the population. Poor weather conditions for the 2011 survey may have resulted in an underestimate of the population (DFO, 2010a). With only two reference points for this population, additional research will be important for confirming trends. It is expected as more research is conducted, the model will need to be updated as appropriate.

Chapter 4

Continuation of environmental drivers into the future reveals the further deviation from a benthic to a pelagic dominated ecosystem. Biomass changes in lower trophic levels up to ﬁsh are a result of further shifts from ice algae

210 7.2. Hudson Bay to pelagic production. Harvest is an important factor in determining the declines of marine mammal species. Species previously identified to show slight declines due to harvest, cannot continue to be harvested at current rates. Narwhal, eastern Hudson Bay beluga, polar bears, and walrus, will be removed from the ecosystem within the model if current harvest levels continue into the future, while harp seals, ringed seals, harbour seals, and beluga (western Hudson Bay and James Bay stocks) are more robust to hunting pressure. Interestingly, with the changes in species composition the trophic level of the ecosystem remains constant from the 1970s to 2069. Even with large reductions in top predators, the ecosystem remains stable and model results show slight increases in biomass in the future. The increase in total biomass is a result of continued increases in zooplankton piscivorous fish. The larger, more stable marine mammal stocks increasing over time compensate for reductions in others and prevents declines in ecosystem trophic levels. While future simulations present an interesting insight as to potential future states of Hudson Bay, research into future impacts of climate change would be futile without a better understanding of the current ecosystem. Rather than recommend research activities directed into the future, energy would be better spent gaining a firmer grasp on the current ecosystem. How- ever, that being said, the past and future models identify the vulnerability of certain marine mammal stocks to current and future over-harvest. It would be wise for managers to focus on the current harvest levels of these stocks to ensure their continued survival.

Chapter 6

Simulation results from chapter 6 estimates the total economic use values be a negative value of $9399 for the beluga hunt and a positive value of $133,278 for the narwhal hunt in 2007 for Nunavut Hudson bay communities. As costs were calculated for each hunt independently, cost sharing analysis reveled that if hunting costs were shared with one other hunting activity the total use value would increase to $266,504 for beluga and $321,500 for narwhal

211 7.2. Hudson Bay hunts. Narwhals provide a higher use value per whale than do belugas due to the added value of their tusks. Despite this, the total revenue was higher for the beluga hunt as more belugas belugas are harvested than narwhals. More communities harvested belugas in this study leading to greater costs across communities. When these values are broken down on a per capita basis, the economic use value for beluga was negative $1 and for narwhal was $44. One possibility for low values is due to errors in parameter estimation. While this is one of a limited number of economic assessments on hunting activities in the Canadian Arctic, parameter estimates were not easy to come by. Future research into obtaining more precise parameter estimates in addition to expanding the model will be useful in developing the overall understanding of hunting activities. One area lacking understanding, highlighted throughout this research, was the use of subsidies for hunting gear. As Nunavut is newly established, this may be one of the main reasons for lack of transparency or literature on the topic of subsidies. Contacts working for the Canadian government struggled to identify how subsidies are regulated in the north. This would be an important addition to the model, and research in this area would beneﬁt many other areas of research. The most interesting results from this chapter is the low values indicate that harvest may be driven by non-economic factors such as cultural importance. Many studies, mainly anthropological, have highlighted the importance of hunting for cultural identity for northerners (Freeman et al., 1998; Freeman, 2005). The economic analysis provided supports this theory. Due to the low per capita economic use value, it is highly possible non use values provide motivations for hunting.

Recommendations to Management for Hudson Bay

Managers will have to face decisions on how to navigate within their limits, not only the Nunavut and soon to be established Nunavik governments, but within the realm of their capabilities and managers. Of the two main threats studied (climate change and hunting), only harvest can be controlled

212 7.3. Antarctic Peninsula by management. Climate does impact the ecosystem, but that is out of the realm of control for managers. The focus is on harvest and if altering the current harvest levels for HB is in alignment with management objectives. Ecologically, it would prevent the declines of narwhal, eastern Hudson Bay beluga, polar bears, and walrus. However, there are many communities who rely on subsistence harvest of these and other species for food and cultural identity. Sustainable harvest is ideal for ﬁsheries in order to perpetuate the resource so it can continue to be harvested, thus increasing the value over longer time frames. If motivations for hunting are derived from cultural values as suggested by chapter 6, management will need to take this into consideration. Preservation of resources for future generations may come at a cost to current hunting activities. Both the Nunavut and Nunavik land claims agreements state principles of conservation to maintain the natural balance of ecological systems while providing continued access to hunting (Nunavut Land Claims Agreement, 1993; Anonymous, 2006). Species shown to decline in the past or future model simulations due to hunting will require reductions in catches if the populations are to be sustained long term. Both land claims agreements also indicate it is ultimately the governments’ responsibility to manage wildlife. Decisions by the federal government (DFO) to restrict hunting have yet to occur in Hudson Bay, however action may be required in the future.

7.3 Antarctic Peninsula

Chapter 3

Chapter 3 describes the Ecopath model for the Antarctic Peninsula and Ecosim simulations recreating past changes in the ecosystem. The first main finding was the overestimation of krill in the diets of predators. It is possible that due to the high variability of diet studies (Hyslop, 1980), the contributions of krill to higher trophic levels may be lower than the literature indicates. Recreation of past trends identified sea surface temper-

213 7.3. Antarctic Peninsula ature as a suitable driver for warmer water species. This gave a lower sum of squares values than did simulations for the Southern Oscillation Index, air temperature, or open water extent, resulting in a better ﬁt for the salp group. Simulations of past changes show an overall decline in primary production as ice algae declines. This results in lower krill biomass and lower biomasses of predators. Under a constant climate scenario in the model, higher levels of marine mammals could be supported indicating they may not be at carrying capac- ity. This would imply that since marine mammals stock had decreased due to large harvest pressure, under a scenario where there are more resources (krill) available, the ecosystem could withstand more mammals. However, under the past conditions recreated, marine mammal species decline due to the environmental impacts on krill. Increasing past catches to current quota levels throughout the simulation shows slight diﬀerences from the past recreated scenario (±3% of biomass) indicating environmental factors have higher impacts on the ecosystem.

Chapter 5

Future simulation continued in chapter 5 identiﬁed further reductions in sea ice, ice algae, krill, and krill predators. Declines in the ecosystem continued from the past model, with mean trophic level remaining stable indicating declines in biomass were even across all trophic levels. Environmental drivers were primarily responsible for the declines of krill when considering current harvest levels. When catches were increased to quota levels, further reductions in krill ensued. Harvest scenarios where future catches are at current quota levels and 25% of the catch is taken from the juvenile krill group cause an instability within the model. Length frequency distributions of krill catches (Jackowski, 2002) show immature krill are taken, and appear to be less than 25%, but the exact contribution to the total catch is unknown. Copepods in the model have the ability to increase at varying degrees in future scenarios. The scenarios, harvest scenario H2b in articular, highlight

214 7.3. Antarctic Peninsula the potential for copepods to replace krill and other prey items for some predators (myctophids, small deep demersals, P. antarcticum and some marine mammals). Omnivorous organisms such as krill demonstrate potential dietary reduction in primary producers and increases in copepods, thus increasing their trophic level. This would then increase the trophic level of krill predators, as an overall lengthening of the food chain. Increased winter based studies may highlight seasonal shifts in predator diets away from krill, as the model suggests. Numerous studies have been completed assessing the impacts of diﬀerent environmental variables on krill survival (Atkinson et al., 2004; Ross et al., 2008; Lee et al., 2010; Flores et al., 2011), and will likely continue due to the importance of krill in the food web. Selected future model scenarios suggest copepods as a replacement for krill, future studies should include or focus on copepods or other zooplankton groups to identify their importance in the food web. Stable isotope analysis, particularly changes over time, would provide a useful comparison to model results, speciﬁcally for krill.

Recommendations to Management for the Antarctic Peninsula

Currently, CCAMLR utilizes a krill yield model to determine harvest levels and to prevent irreversible damage to the ecosystem due to over-harvest (CCAMLR, 1980). However, the current model is a single species approach which incorporates predator demands and environmental variability. Man- agement should consider expanding their current krill yield model to incorporate harvest on juveniles, and perhaps build into reporting strategies a way to determine the sexual maturity of krill so the contribution of juveniles can be estimated. Indirect effects of krill harvesting have been difficult to incorporate into models in the past (Constable et al., 2000). Past and future simulations in the thesis can be used to tease out some of the indirect effects as to provide this information to managers. Additional, more focused simulations could be constructed for specific management issues if needed now that the model structure exists.

215 7.3. Antarctic Peninsula

CCAMLR management strategy encompasses an ecosystem approach and strives to ensure there are enough resources to meet ecosystem demands when considering quotas for krill and other species (CCAMLR, 1980). However it appears as changes in climate progress, the impacts to krill will become more prominent leaving less to be harvested without aﬀecting predators. While harvest is not the primary cause of krill declines, it does further reduce a strained resource. Managers will be forced to choose whether to protect as much as the resource as possible to help thwart the eﬀects of climate change, or continue harvesting.

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286 Appendix A

Hudson Bay Ecosystem Model Parameters and Details

A.1 Model Parameters by Functional Group

Marine Mammals

All marine mammals which inhabit the model area were included in the model. In addition, many species have been shown to be representatives from genetically distinct stocks, and therefore have been split into individual functional groups. For example, there are three stocks of polar bears within the model area, each with diﬀering population trends and hunting quotas, and were therefore considered diﬀerent stocks and functional groups within the model. Four species of cetaceans (bowhead whales, narwhals, belugas, and killer whales) are seasonal residents in Hudson Bay. For these functional groups their impact on the ecosystem is relative to the amount of time spent in the area and the proportion of annual feeding occurring during their time in Hudson Bay. A weighted biomass was designated to each of these groups to represent their respective impact on the ecosystem, so that if a group of whales resided in Hudson Bay half of the year and half of their feeding occurred during this time, then their weighted biomass would be half of the total population biomass (50%) to account for this. Individual estimates are given within functional group parameters. For all marine mammal groups the following equations were used to calculate input parameters (parameters for all marine mammals are in table A.1).

287 A.1. Model Parameters by Functional Group

Biomass was calculated by multiplying the number of individuals by average weight of individuals (in tonnes), then divided by the model area (km2). Mortality rates (P/B ratios) were calculated for each species using the life table based on natural mortality (Barlow and Boveng, 1991)., and compared to published values where available (full equations for P/B calculations are available in appendix B). Mortality from hunting was calculated as the biomass harvested/total biomass, and was added to the natural mortality to give the ﬁnal P/B ratio. Q/B: Consumption (Q/B) was calculated using equation A.1 (Hunt et al., 2000; Guenette, 2005);

E = aM 0.75 (A.1)

where E in the energy required per day (Kcal/day), M is the mean body weight (in Kg) and a is a coeﬃcient representing each group of marine mammals (a=320 for otariids, 200 for phocids, 192 for mysticetes, 317 for odontocetes, and 320 for sea otters). Energy contents of food items was provided by various authors as summarized in Cauﬀope and Heymans (2005).

288 Table A.1: Input parameters for marine mammal functional groups. Mean weight is provided in Kg and Longevity is provided in years. Calculated (calc.) values and values used in the model are presented in the case they diﬀer. Mortality (M) is presented as an annual value (y−1) for both the calculated natural mortality and hunting mortality. Consumption/Biomass (Q/B) and Production/Biomass (P/B) are also presented as annual values (y−1).

Species Pop Source Mean Source Long- Source M Hunt Model Calc. Model Size Weight evity (calc.) M P/B Q/B Q/B Polar Bear WHB 1200 Lunn et al. (2002) 300 Stirling and Parkinson 25 Stirling (2002) 0.096 0.033 0.129 3.029 2.08 (2006) Polar Bear SHB 1000 Lunn et al. (2002) 300 Stirling and Parkinson 25 Stirling (2002) 0.096 0.058 0.154 3.109 2.08 (2006) Polar Bear Foxe 3000 Aars et al. (2005) 300 Stirling and Parkinson 25 Stirling (2002) 0.096 0.024 0.12 2.849 2.08 (2006) Killer Whale 1 20 Higdon and Ferguson 4689 Ford (2002) 80 Ford (2002) 0.048 0.051 0.151 4.998 4.998 (2009) Narwhal 1 2710 Richard (1991) 1300 Heide-Jorgensen 115 Garde et al. (2007) 0.083 0.008 0.084 18.696 26.182 (2002) Bowhead 1 64 Higdon 2009 (unpub- 31076 Trites and Pauley 200 George et al. (1999) 0.018 0.003 0.021 5.475 5.475 lished data) (1998) Walrus N 2500 Mansﬁeld and 1037.5 Kastelein (2002) 35 Kastelein (2002) 0.141 0.031 0.172 41.238 47.123 St Aubin (1991) Walrus S 500 Richard and Campbell 1037.5 Kastelein (2002) 35 Kastelein (2002) 0.088 0.009 0.097 29.56 33.778 (1988); COSEWIC (2006) Bearded Seal 15000 Lunn et al. (1997) 275 Kovacs (2002) 25 Kovacs (2002) 0.131 0.045 0.176 13.848 14.262 Harbour Seal 1000 assumed 76 Burns (2002) 29.5 Trites and Pauley 0.123 0.002 0.125 18.612 18.612 (1998) Ringed Seal 600000 Smith (1975) 42.5 Trites and Pauley 43 Miyazaki (2002) 0.15 0.008 0.158 16.05 17.272 (1998) Harp seal 8000 Assumed (Ferguson 130 Lavigne (2002) 30 Lavigne (2002) 0.112 0.014 0.126 15.66 15.66 pers. comm) Belgua E 1 4200 Hammill (2001); Gos- 725 DFO (2002a); 50 Harwood et al. 0.044 0.032 0.0662 21.448 21.448 selin (2005); Hammill NAMMCO (2005a) (2002); Stewart et al. et al. (2009) (2006) Belgua W 1 50,000 COSEWIC (2004b); 725 DFO (2002a); 50 Harwood et al. 0.0587 0.005 0.064 16.713 16.713 NAMMCO (2005a) NAMMCO (2005a) (2002); Stewart et al. (2006) Beluga James 1 1842 Gosselin et al. (2002) 725 DFO (2002a); 50 Harwood et al. 0.057 0.019 0.0872 16.623 16.623 NAMMCO (2005a) (2002); Stewart et al. (2006) 1 indicates biomasses were adjusted to 50% to account for roughly 50% of their time spent in the model area. 2

289 indicates P/B for Eastern Belugas and James Belugas also account for migrations which were added in the ﬁtting process A.1. Model Parameters by Functional Group

Polar Bears (Ursus maritimus)

Three of the nineteen polar bear populations (Paetkau et al., 1999) overlap with the Hudson Bay ecosystem model area; the Western Hudson Bay population, the Southern Hudson Bay population, and part of the Foxe Basin population (see Stirling et al., 1999, for polar bear stock delineations). These three populations were included in the model under different functional groups corresponding to each population (Western Hudson Bay, Southern Hudson Bay, and Foxe Basin). Being at the southern range of their limits in HB, climate change is believed to be an important factor in determining the health of these populations. Since polar bears rely on ice for foraging, extension to the ice free summer caused by melting is believed to increase nutritional stress. In addition, because these southerly populations already experience longer summers than their northern counterparts, they are thought to be more vulnerable to declines in sea ice (Stirling and Derocher, 1993; Stirling et al., 1999). The Foxe Basin (FB) and Western Hudson Bay (WHB) populations are believed to be declining, while there have not been enough surveys to determine trends in the Southern Hudson Bay (SHB) stock (Aars et al., 2005). In addition each population is subjected to different hunting pressures depending on the communities within their respective ranges. While diets vary among populations, ringed seals are the most important food item in all polar bear populations, followed by bearded and harp seals (Peacock et al., 2010). Polar bears have also been known to take walrus, beluga, narwhal, seabirds, and waterfowl (Stirling, 2002). Scat analysis of western and southern HB polar bears form the late 1960s indicated foraging on birds (primarily from the family Anatidae- ducks, swans, and geese), mussels, urchins, other unidentifiable invertebrates, and berries in the late summer and autumn (Russell, 1975; Derocher et al., 1993). Although it is likely that these prey items are also consumed by the Foxe Basin population, it is believed the WHB and SHB may consume a greater portion of birds, invertebrates, and plants in their diets. Polar bears were traditionally hunted for food and clothing, a tradition

290 A.1. Model Parameters by Functional Group which still exists today. Quotas have been imposed on each of the stocks by corresponding jurisdictions in Nunavut, Ontario, Manitoba, and Quebec (Peacock et al., 2010).

Western Hudson Bay Polar Bears The western HB polar bear population has been declining since 1981. The decline is believed to be caused by a lengthening of the ice free season (summer) which has led to increased nutritional stress (Stirling et al., 1999). The increased open water season is correlated with poor condition especially in female polar bears (Stirling et al., 1999). The population was estimated at 1200 bears based on an estimate from 1987 (Lunn et al., 2002), giving a biomass for the region of 0.00046t·km−2. In 2004 the population is believed to have dropped to 935 animals (Aars et al., 2005). An average catch of 44 bears during the 1980s (Lee and Taylor, 1994) has since increased slightly to 46.8 bears for the 1999-2004 period (Aars et al., 2005). The 2005 quota for the WHB polar bear population was 56 bears (Aars et al., 2005). Diet was set to 1% polar bears (Western Hudson Bay), 0.5% northern walrus, 12.5% bearded seals, 0.1% harbour seals, 61.9% ringed seals, 3% harp seals, 10% western beluga whales, 2% seabirds, 1% each echinoderms and bivalves, 7% other benthos.

Southern Hudson Bay Polar Bears The SHB polar bear population was estimated at 1000 bears in the 1980s (Lunn et al., 2002), giving a biomass of 0.000383t·km−2 for the entire region. There have been no estimates of this population since, therefore the estimate of 1000 bears was used for the starting 1970s biomass. The diet for SHB polar bears was set to: 1% SHB polar Bears (to account for cannibalism), 0.5% southern walrus, 12.5% bearded seals, 0.1% harbor seals, 62.4% ringed seals, 3% harp seals, 0.5% eastern belugas, 6.5% James Bay belugas, 7% seabirds, 2% echinoderms, 2% bivalves, and 2.5% other benthos. The average catch of SHB polar bears for the 1980s was 68 (Lee and Taylor, 1994), and with no previous records available, this values was assumed to be the catch for 1970.

291 A.1. Model Parameters by Functional Group

Foxe Basin Polar Bears The FB polar bear population has shown a decrease from 3000 bears (1970s) to 2100 (1996), and then a slight increase to 2300 in 2004 (Aars et al., 2005). This population is not fully within the model limits so the 1970s abundance would yield a biomass of 0.000986t·km−2, however it was assumed only 20% of the population was geographically located within the model area, so the biomass was adjusted to 0.000197t·km−2. Average catches for the 1980s were 142 bears (Lee and Taylor, 1994). This value was used as the catch in 1970, although again it was also adjusted to 20% of its value to account for the percentage taken from within the model area. The diet for FB polar bears is believed to contain less birds and invertebrates and more seals (Russell, 1975) and was therefore set to 0.5% FB polar bears, 20% bearded seals, 1% harbor seals, 59.5% ringed seals, 4% harp seals, 8% western Belugas, 2% seabirds, 1.5% echinoderms, 1.5% bivalves, and 2% other benthos.

Killer Whales (Orcinus orca)

There has been an observed increase in the number of killer whales present in Hudson Bay since the 1950s which has been linked to the decreasing ice cover in the region (Higdon and Ferguson, 2009). Killer whales move into Hudson Bay through Hudson Strait in the summer when the ice has melted enough to allow them to travel through, and they leave before the annual freeze-up. It is believed they travel into the area following other marine mammal species as food, although a determined ecotype has not been established for these animals. Inuit knowledge suggests killer whales were not present prior to the mid-1900s but are now observed on a regular basis (Gonzalez, 2001). A photo identification project established in 2005 has identified 67 unique individuals in the Eastern Arctic (Peterson et al., 2009). The 1970s population was set to 20 individuals or a biomass of 0.000025 t·km−2 based on the conservative population estimate for the 2000s of at least 67 individuals, and sightings which have increased nearly fivefold since

292 A.1. Model Parameters by Functional Group the 1970s (Higdon and Ferguson, 2009). Although killer whales only enter HB during the ice-free season, it was assumed that for the proportion of the population which do, they feed completely on the species in the model area. Therefore no adjustments to the biomass were made. Reported observations of predation consist of marine mammals, although not enough research has been completed to identify this population of killer whales as marine mammal consumers. In addition, reports from killer whales in other areas of Canada have stated observations of whales eating fish (Law- son et al., 2007; Higdon and Ferguson, 2009). The diet was therefore set primarily to marine mammals with some fish and birds being consumed; 8% narwhal, 2.5% bowhead, 6% walrus (3% each north and south walrus), 13% bearded seal, 1.5% harbor seal, 33% ringed seal, 3% harp seal, 22% beluga (1% eastern, 16% western, 5% James Bay), 3% seabirds, 0.5% Atlantic Salmon, 3% gadiformes, 2% sculpins/zoracids, 0.5% sharks/rays, 1% other marine fish, and 1% cephalopods (Gonzalez, 2001; Higdon, 2007; Higdon and Ferguson, 2009). Based on increased sightings in Higdon and Ferguson (2009) for Hud- son and James Bays, sightings of killer whales was assumed to be directly proportional to the number of killer whales present. A review of literature by Higdon (2007) summarized reported kills of killer whales from 1957 onwards in the eastern Canadian Arctic. Since killer whales are occasionally harvested, hunting mortality for the first year was set intentionally low; to the equivalent of half the biomass of one whale to give a hunting mortality of 0.103y−1. This combined with the natural mortality led to a P/B of 0.151 y−1 to be used in the model.

Narwhal (Monodon monoceros)

The Northern Hudson Bay stock of narwhal is the smallest of three narwhal stocks (Northern Hudson Bay, Baﬃn Bay, and Greenland Sea) in the Arc- tic (COSEWIC, 2004a). Narwhals are found near the Repulse Bay area of Hudson Bay in the summer months, and migrate to the Labrador Sea for the winter, spending roughly half of the year within the HB model area.

293 A.1. Model Parameters by Functional Group

Although the wintering area for the Hudson Bay stock and the Baffin Bay stock overlap, summer site fidelity indicates they are different stocks (West- dal et al., 2010). The stock for Hudson Bay was estimated to be 1355 individuals in 1984 (Richard, 1991), however this analysis did not account for submerged animals during the sampling, and should be doubled (to 2710) to more accurately represent the population. An estimate of 1780 whales for the population in 2000, also under-representative due to diving animals was corrected to 3500 whales, which is believed to be a more accurate value (COSEWIC, 2004a). Biomass and catches were adjusted to 50% of original values to accommodate for time spent and feeding outside of the model area. Narwhal diets in HB are thought to be focused on Arctic cod, squid, and crustaceans, also including demersal species and invertebrates (Heide- Jorgensen, 2002; COSEWIC, 2004a; Stewart and Lockhart, 2005). The diet was set to 1% Arctic char, 1% Atlantic salmon, 25% gadiformes, 15% sculpins/ zoarcids, 12% capelin, 10% other marine fish, 2% brackish fish, 10% cephalopods, 5% macro-zooplankton, 4% euphausiids, and 15% crustaceans.

Bowhead (Balaena mysticetus)

The eastern Canadian Arctic bowhead whales are one of two populations worldwide, with the other being in west Greenland. Previously the Canadian population was believed to be two stocks (George et al., 1999), although genetic sampling has shown not to support this idea suggesting whales are from the same stock (Ferguson, 2007).Bowheads are the largest marine mammals within the HB ecosystem, with weight estimates ranging from 54,000kg up to 68,000kg or higher for adult individuals (Rugh and Shelden, 2002; Amer- ican Cetacean Society, 2004) and can live for over 200 years (George et al., 1999). They have been an important source of food for historic cultures located in Hudson Bay starting with the Thule near 1000 AD (Higdon, 2008). Annual migrations coincide with the ice-free season in HB, where whales move into HB around April to May and leave in September. Although the

294 A.1. Model Parameters by Functional Group population has been estimated to be as high as 625 individuals in the 1860s for the HB region, it had dropped as low as or lower than 100 individuals in the late 1800s to early 1900s due to commercial whaling. Since reaching a low in the early 1900s the population has increased with model estimates of 300-400 whales (Higdon 2008 unpublished data). Survey data put the recent HB portion of whales at a minimum of 75 (not accounting for submerged animals at the time of the study) while the Foxe Basin portion of the study identiﬁed to be between 256-284 (again not accounting for submerged animals) whales in 1994 (Cosens and Innes, 2000). These are now believed to be from the same stock with diﬀering summering grounds, and some sex segregation with mostly cow calf pairs in HB (Higdon and Ferguson, 2010). The historical model estimates the 1970s population to be 319 whales, and it was assumed that roughly 20% of this population will enter Hudson Bay, as based on a 1994 survey where there were 75 whales in HB and 284 in Hudson Strait observed (DFO, 1999), giving an estimate of 64 whales. The biomass was then set to 0.0109t·km−2. The diet of bowhead whales is believed to consist primarily of copepods and euphausiids with other zooplankton (mysids, gammerid amphipods) and benthic crustaceans being consumed (Lowry et al., 1987; Rugh and Shelden, 2002). The diet was set to 5% macro-zooplankton, 30% euphausiids, 45% copepods, 5% crustaceans, 1% other meso-zooplankton, 5% micro- zooplankton, 2% marine worms, 1% echinoderms, 1% bivalves, and 5% other benthos.

Atlantic Walrus (Odobenus rosmarus)

Walrus are year round inhabitants of HB, surviving the winter on the ice. They utilize the sea ice as a platform for breeding, and rely on polynyas in order to feed throughout the winter (Stirling, 1997; NAMMCO, 2005a). Two of the ﬁve recognized stocks of walrus are located partially or fully within HB; the south and east Hudson Bay stock which is completely contained in the model (referred to as Walrus South in the model), and the Hudson Bay-Davis Strait stock (referred to as Walrus North in the model) where the

295 A.1. Model Parameters by Functional Group lower portion of the range reaches into the northern part of the model area (DFO, 2002b; COSEWIC, 2006). See Stewart (2008) for stock delineations. There are no complete stock assessments for any of the four walrus stocks, however estimates are presented for each of the HB stocks (DFO, 2002b). These stocks were split into two functional groups as they are hunted by diﬀerent communities, and have diﬀerent dietary habits.

Walrus N The Walrus North species group represents the Hudson Bay- Davis Strait stock. This population has been estimated to contain 3000- 4000 animals in the mid 1970s (Richard and Campbell, 1988). This estimate represented the population within the entire stock range. However a 1976 survey for the Southampton/ Coates Islands region of northern Hudson Bay estimated 2370 animals in this smaller area (Mansﬁeld and St Aubin, 1991). The population within the model was set to 2500 animals, a conservative estimate, or 0.00274t·km−2 to represent the animals found within the model area from this population. The diet for these animals consists mainly of benthic invertebrates (bivalves, gastropods, holothurians, polychaetes, and brachiopods), with bivalves contributing to nearly half the diet by weight (Fisher and Stewart, 1997; Kastelein, 2002; Born et al., 2003). Within the model the diet was set to: 2% gadiformes, 1% sculpins/zoarcids, 3% other marine ﬁsh, 6% crustaceans, 10% marine worms, 25% echinoderms, 40% bivalves, 13% other benthos.

Walrus S The Walrus South functional group represents the south and eastern HB stock, which is completely contained within the model area. The population has been estimated to be roughly 410 animals in the late 1970s from surveys at 2 locations in southern HB (310 and 100 walruses), although the reliability of this estimate has been questioned (Richard and Campbell, 1988; COSEWIC, 2006). Due to lack of better estimates a value of 500 animals was used for the 1970s starting biomass. Although there are no complete surveys, hunters have reported fewer walruses being observed than in the past (DFO, 2002b), indicating a declining population. The biomass

296 A.1. Model Parameters by Functional Group was set to 0.001t·km−2. This stock has been shown to be feeding at higher trophic levels than the other walrus group through stable isotope analysis. While these walruses do still consume bivalves and other invertebrates, they are also feeding on ringed seals and occasionally bearded seals (Muir et al., 1995, 2000). The diet was set to 0.1% bearded seals, 3.9% ringed seals, 8% gadiformes, 1% sculpins/ zoarcids, 5% other marine ﬁsh, 7% crustaceans, 10% marine worms, 15% echinoderms, 40% bivalves, 10% other benthos.

Bearded Seal (Erignathus barbatus)

Bearded seals are year round inhabitants, using the pack ice and sea ice to haul out. They tend to be found near polynyas or other areas with open access to the water during the winter, and generally inhabit areas with a depth of 200m or less for foraging (Angliss and Outlaw, 2006). There have been no studies to suggest there is more than one stock of bearded seals in HB, and although there is no estimate for all bearded seals in HB, surveys have been conducted for the western portion of HB. Lunn et al. (1997) estimated 12900 and 1980 bearded seals for the western portion of HB in 1994 and 1995 respectively based on aerial surveys. It is believed the conditions of the survey played a large role in the discrepancies between estimates. The population for the 1970s was set to 15000 bearded seals for the entire model area or 0.0037t·km−2, slightly higher than the 1995 estimate. This was set as a conservative estimate for the entire region as there are no known trends for bearded seals, and the surveys did not cover the entire region. It is believed that there may be declines in the bearded seal population as they are a prey item for polar bears, and declining polar bears (Western HB and Foxe Basin) have been shown to be declining possibly because of decreased ringed and bearded seals (Lunn et al., 1997). Hunting of bearded seals is not regulated, with few studies on estimates of numbers hunted (see section A.2). Bearded seals are benthic feeders with bivalves and crustaceans being the most abundant items in the diet, but ﬁsh contributing the highest percent of

297 A.1. Model Parameters by Functional Group weight (Smith, 1981; Finley and Evans, 1983). Shrimp are more important to newly weaned seals while adults diets are most likely focused on clams (Young et al., 2010). The diet was set to 3% Arctic char, 2% Atlantic salmon, 20% gadiformes, 5% sculpins/zoarcids, 17% capelin, 4% sandlance, 5% other marine ﬁsh, 2% brackish ﬁsh, 1% cephalopods, 1% macro-zooplankton, 25% crustaceans, 2% marine worms, 8% echinoderms, 5% other benthos.

Harbor Seal (Phoca vitulina)

Harbor seals in Hudson Bay are known to reside in the marine environment as well as lakes which drain into HB (Mansfield, 1967b; Smith et al., 1996). The lake seals are not thought to migrate into the marine environment, and are therefore excluded from the model. Although there are no estimates for harbor seals in Hudson Bay, freshwater populations have been estimated between 100-600 seals for specific regions such as Lacs des Loups Marins, Quebec (Smith and Lavigne, 1994). Harbor seals are thought to be one of the least abundant seals in HB therefore the abundance was set to 1000 seals or 0.001t·km−2 (Ferguson pers. comm.). The diet of harbor seals consists primarily of benthic fish, invertebrates, squid, and crustaceans (Bigg, 1981). For the model the diet was set to 10% gadiformes, 8% sculpins/zoarcids, 20% capelin, 20% sandlance, 10% other marine fish, 6% brackish fish, 2% cephalopods, 2% macro-zooplankton, 2% euphausiids, 10% crustaceans, 3% marine worms, 3% echinoderms, and 4% other benthos.

Ringed Seal (Pusa hispida)

Ringed seals are the most abundant seals with a year round distribution in HB. Tagging studies show their ability to travel around Hudson Bay in a matter of weeks. However, seals tagged within Hudson Bay have not been shown to leave the region during the duration of the tagging study (Luque and Ferguson, 2008). Because these seals have been shown to travel large distances around HB, all ringed seals in the model area were considered one stock. Recent studies estimated the population size at 73170 in 2007

298 A.1. Model Parameters by Functional Group and 33701 in 2008 for the western portion of HB (DFO, 2009a) representing only a small portion of the model area. Densities estimated varied from 0.97±0.06 seals·km−2 in 2007 to 0.49±0.04 seals·km−2 in 2008 for western HB ranging from Arviat to Churchill (Chambellant and Ferguson, 2009). If seals were distributed evenly throughout the area the population estimate would range between 450,000 and 900,000 seals. 1975 estimates from projected population at 61000 seals for James Bay and 455,000 from Hudson Bay (Smith, 1975). The population for the 1970s was set to 600,000 seals, or 0.0469t·km−2. In general ringed seals feed primarily on Arctic cod and other pelagic fish along with amphipods (DFO, 2009a). In the Baffin Bay region the diet is dominated by Arctic cod and Polar cod (Holst et al., 2001), but in HB sandlance, euphausiids, and capelin are the most frequent (Chambel- lant, 2010). The diet for Hudson Bay was set to: 18% gadiformes, 10% sculpins/zoarcids, 20% capelin, 30% sandlance, 8% other marine fish, 2% cephalopods, 2% macro-zooplankton, 2% euphausiids, and 8% crustaceans.

Harp Seals (Phoca groenlandica)

Harp seals are the least abundant of the seal species found in Hudson Bay, although there are no estimates for the abundance in this region. They enter Hudson Bay through Hudson Strait after the break-up of ice in the summer from the Gulf of St Lawrence and southeastern Labrador and leave the area before the freeze up in the fall (Stewart and Lockhart, 2005). Population estimates for harp seals in Newfoundland in the 1970s were between 700,000 to 1.5 million (Lavigne, 1979), however in addition to summering in HB, many animals move to Lancaster Sound, Baffin Bay, Hudson Strait, or Foxe Basin (Mansfield, 1967a). For the model the population within HB was estimated to be 8,000 (Ferguson pers. comm.) or 0.001t·km−2. The diet of harp seals from Hudson Strait consists primarily of capelin, and is likely to be similar to the diet of seals within Hudson Bay. Other fish and invertebrate species found from stomach contents were: Arctic cod, sculpin, flatfish, rock cod, mysids, crustaceans, decapods, and other inver-

299 A.1. Model Parameters by Functional Group tebrates (Beck et al., 1993). The diet was set to: 2% Atlantic salmon, 2% gadiformes, 1% sculpins/zoarcids, 86% capelin, 5% other marine ﬁsh, and 4% crustaceans.

Beluga (Delphinapterus leucas)

Stocks of beluga whales are not fully known for the Hudson Bay region. The North Atlantic Marine Mammal Commission suggests there are 6 groups of belugas within Hudson Bay (NAMMCO, 2005a), while genetic studies suggest there are most likely two or three (de March and Postma, 2003), based on where whales are hunted or spend a majority of their time. de March and Postma (2003) demonstrate that some belugas harvested from Sannikiluaq are genetically different from the eastern HB and western HB populations. In addition it is possible that belugas harvested from Churchill are also a different stock, although this was not confirmed through genetics. Tagging studies have identified mixing between these populations, making divisions more difficult (Richard and Orr unpublished manuscript as cited in Stewart and Lockhart 2005). For the model three functional groups of Belugas were created to represent all populations within Hudson Bay: Eastern HB Bel- uga, Western HB Beluga, and James Bay Beluga. Although mixing between these groups is not well known, for modeling purposes it was assumed there are three separate stocks. As belugas do not spend the winter in HB, the biomass and catches were adjusted to 50% to account for six months within the model area. The general diet of belugas has been noted as consisting primarily of fish species (with pelagic fish being important), benthic invertebrates and squids (Pauly et al., 1998b). In the Beaufort sea belugas feed primarily of cod (Loseto et al., 2009), while west Greenland belugas consume squid, molluscs, and myctophids in addition to cod (Heide-Jorgensen and Teilmann, 1994). Other noted prey items include crustaceans, worms, and sculpins (Stewart and Lockhart, 2005), with capelin as an important component to the diet of eastern and James Bay belugas (Kelley et al., 2010).

300 A.1. Model Parameters by Functional Group

Beluga East HB Belugas residing in eastern Hudson Bay are considered part of the Ungava and Hudson Bay stock, which is currently listed as endangered by COSEWIC (NAMMCO, 2005a). The eastern HB population winters in northern Labrador and makes it migration past Ungava Bay and down the eastern coast of HB to its summer location ranging from Kuu- jjuaraapik to Inukjuak (DFO, 2001). There appears to be a strong genetic basis for designating belugas of Eastern Hudson Bay as a separate population and increasingly good evidence that they contribute to the harvests in Nunavik communities as far as Ungava Bay (COSEWIC, 2004b). Areal transect surveys have shown varying trends in the population (Gos- selin, 2005; Gosselin et al., 2009), however the general trend from surveys and modeling is the population has declined from roughly 4000 whales in 1985 to 2000-3100 whales in 2008 (Hammill, 2001; Gosselin, 2005; NAMMCO, 2005a; Hammill et al., 2009). These declines are thought to be caused primarily by hunting, although noise pollution, river dams, and environmental pollution are also considered factors (DFO, 2008). This population was listed as threatened by COSEWIC in 1988, and elevated to endangered status in May 2004 (COSEWIC, 2004b). Inuit communities have noted many of the rivers previously utilized by belugas along Hudson Strait and eastern Hudson Bay are no longer used. They believe noise is keeping the whales further oﬀshore in these areas (COSEWIC, 2004b). The biomass for the 1970s population was set to 0.00207t·km−2 or 2100 whales (4200 whales at 50% of the time in the model area). The diet was set to: 2% Atlantic salmon, 8% gadiformes, 10% sculpins, 10% capelin, 5% cephalopods, 2% brackish ﬁsh, 15% euphausiids, 8% copepods, 17% crustaceans, 8% marine worms, and 15% other benthos.

Beluga West HB The western Hudson Bay beluga population arrive through Hudson Strait to Churchill, Nelson, and the Seal river estuaries through the summer (COSEWIC, 2004b). This population appears to be relatively abundant, although surveys have been sporadic (i.e. 1987 and 2005). COSEWIC (2004b) has designated this population as special concern due to potential substantial removals by hunting throughout its range and

301 A.1. Model Parameters by Functional Group concerns with hydroelectric dams and shipping. Estimates show the population as stable. Earlier surveys in 1985 and 1987 estimated the population at 23000 and 25100 whales respectively, while not accounting for submerged animals at the time of the survey (COSEWIC, 2004b; NAMMCO, 2005a). A 2004 estimate of 57300 whales suggests the population has not changed, as the uncorrected number from this survey is similar to the uncorrected abundances from previous studies (Richard, 2005). The 2004 survey also identified an additional 1300 animals along the Ontario coast and 700 along northern HB, however it was not known what stock these whales belonged to. Little genetic testing has occurred on the western HB population as it has been assumed to be one large stable population (COSEWIC, 2004b; Luque and Ferguson, 2010). The population of WHB belugas was set to 25000 whales (50000 whales at 50% of the time in the model area) to yield a biomass of 0.0247 t·km−2. In western Hudson Bay belugas feed on capelin (Mallotus villosus), river fish, marine worms and squids (Culik, 2004), with capelin as an import contribution to the diet (Kelley et al., 2010). WHB belugas were assumed to feed on a slightly higher diversity of zooplankton due to the increased abundance found in WHB based on zooplankton samples (Harvey et al., 2006). The diet was set to 5% Arctic char, 2% Atlantic salmon, 15% gadiformes, 3% sculpins/zoarcids, 20% capelin, 1% sandlance, 4% other marine fish, 4% brackish fish, 5% cephalopods, 1% macro-zooplankton, 10% euphausiids, 5% copepods, 10% crustaceans, 5% marine worms, and 10% other benthos.

Beluga James Bay It was assumed that the hunting on this population occurs primarily from Sanikiluaq as the whales hunted at this community have been shown to be diﬀerent from the EHB belugas (de March and Postma, 2003). Currently it is not fully known if this population is a separate population or constant mixture of other populations, as they appear to be more closely genetically related to western HB belugas than eastern HB belugas (COSEWIC, 2004b), although closer to eastern HB in proximity. Traditional knowledge indicates that there are some whales which spend the winter in James Bay, however it is not known if this is due to ice entrapment

302 A.1. Model Parameters by Functional Group or not (Stewart and Lockhart, 2005). Whales either remain overwinter in James Bay or migrate from the Quebec coast of HB into James Bay, with some migration around the Belcher Islands (Richard and Orr (2003) unpublished data as cited in Stewart and Lockhart, 2004). Since 2004, eight belugas from James Bay have been ﬁtted with satellite tags, and none have been shown to move into eastern HB (Hammill unpublished data cited in Gosselin et al., 2009). For the model, the James Bay beluga will be treated as its own population, with hunting pressure occurring form the Sanikiluaq (Belcher Island) community, as no harvest occurs within James Bay (COSEWIC, 2004b). Derived estimates of whale abundance have increased from roughly 1842 whales in 1985 to 3141 whales in 1993 to 7901 whales in 2001 (Gosselin et al., 2002). Estimates are considered conservative as they do not account for submerged animals, or those beyond survey view (Stewart and Lockhart, 2005). This apparent increase in the population based on the 2001 survey is too high to be explained by population growth, and is believed to be an artifact of survey coverage, and seasonal movements (COSEWIC, 2004b). A 2004 estimate of 3998 whales was believed to be too uncertain to use for management (Gosselin, 2005). The model population was set to 1842 whales for the 1970s giving a biomass 0.00147t·km−2. This estimate did not account for submerged animals, and should be doubled based on the correction factors of other beluga populations. However, assuming belugas spend 50% of their year in the model area, the abundance of 1842 was used as is for input. The diet is believed to be focused heavily on capelin for this population (Stewart and Lockhart, 2005) and was set to 1% Atlantic salmon, 5% gadiformes, 50% capelin, 5% cephalopods, 10% euphausiids, 5% copepods, 10% crustaceans, 5% marine worms, and 9% other benthos.

Seabirds

The group for birds includes all migratory and year round inhabitants. Most species arrive after the breakup of ice and leave before the freeze up, with a few exceptions of year round inhabitants (Stewart and Lockhart, 2005).

303 A.1. Model Parameters by Functional Group

Some 133 species of birds are recorded to utilize the HB marine ecosystem (appendix C) which funnels southbound migrating birds into James Bay, where the coastal marshes are an important stopover for many species (Stewart and Lockhart, 2005). Biomass for this group was estimated using bird counts from another Arctic area, the Chukchi Sea, Alaska, as Hudson Bay estimates were unavailable. The average number of birds from 1989-1991 in this region was 75 birds km−2 (Johnson et al., 1993). This coupled with the average weight of the bird species found with the Hudson bay area of 867g (Karpouzi, 2005), gave a biomass estimate of 0.065t·km−2. A P/B value of 0.113y−1 was used for natural mortality, based on the seabird population in the Aleutian Islands (Heymans, 2005), although a hunting mortality for HB based on catches of 0.005 y−1 was calculated. The combined P/B value of 0.118y−1 was too low for the model and had to be increased to 0.37y−1 in order to balance the model. The EE was set to 0.95, to let the model estimate Q/B. Diet for this group, was based data provided by Karpouzi (2005), and was set to 2% seabirds, 3% Arctic char, 3% Atlantic salmon, 2% gadiformes, 3% sculpins/zoarcids, 15% capelin, 4% sandlance, 4% other marine fish, 10% brackish fish, 10% cephalopods, 12% macro-zooplankton, 5% euphausiids, 1% copepods, 1% other meso-zooplankton, 2% marine worms, 3% echinoderms, 10% bivalves, 5% other benthos, 5% pelagic detritus. Thick-billed murres have been monitored at Coats Island (in northern HB just southeast of Southampton Island) since 1985, and have shown an annual average increase in population (roughly 1.7% per year). Similar trends for thick-billed murres have been reported at Digges Island (just east of Coats Island at the northern edge of the model area) up until 2000 when the population appears to have leveled off (Gaston et al., 2009a). For the same region glaucous gulls have declined up to 50% (unpublished data cited in Gaston et al., 2009a)). Near the Belcher Islands surveys show the mean number of gull nests declining by 50% since 1980 and slight declines of Arctic terns (only significant declines at 1 of 5 sites surveyed) (Gilchrist and Robertson, 1999). The breeding of thick-billed murres has become earlier (6 days earlier

304 A.1. Model Parameters by Functional Group since 1980), which is believed to be due to an earlier breakup of sea ice (17 days earlier when comparing 1988 to 2007), however it is not believed that changes to breeding cycles will be able to keep up with changes in environmental cycles (Gaston et al., 2009b,a). The diet of thick-billed murres has demonstrated shifts from Arctic cod to capelin as shown in ﬁgure 2.3(Gaston et al., 2003). Although local changes appear to have occurred, it is hard to extrapolate to all bird species from regional studies. No large scale increases or declines have been observed in HB that would apply to all bird species within this group, therefore no assumptions on trends has been made for this model.

Fish

Fish species were determined based on the species named present in Hud- son Bay and/or James Bay in appendix 3 of Stewart and Lockhart (2005). Species listed were categorized based on life history; marine, brackish, estu- arine, diadromous, anadromous, or semi-anadromous. However as the model is defined as the marine ecosystem only species listed as marine and some species defined as brackish were included in the model. There are ten groups of fish in the model, based primarily on familial traits and secondarily on life history characteristics. Species found in each functional group are listed in appendix D.1. As no comprehensive surveys have yet been completed, biomass was estimated for all fish groups, utilizing the ability of Ecopath with Ecosim to solve for one unknown parameter for each functional group. Biomass for all fish groups was estimated by the model using the inputs of P/B, Q/B, EE, and the diets of other functional groups. Total mortality was set to the sum of fishing mortality and natural mortality, with the natural mortality being calculated using the life history tool page in Fishbase (Froese and Pauley, 2008), which provides equation A.2, where M is the natural mortality, L∞ = the maximum length of the fish, and T is the temperature of the water (in ◦C) (Pauly, 1980; Froese and Pauley, 2008). As little information is known about fish in Hudson Bay,

305 A.1. Model Parameters by Functional Group default values provide by Fishbase for L∞ = were used. For temperature, both the average value provided for the species based on temperatures ﬁsh are normally found in (provided by Fishbase), and an average of 0.5◦C were used and calculated values are presented in table A.3. The 0.5◦C value was chosen as it is the average water temperature for this region from 1960-2006, based on a global database of ice and sea surface temperature (SST) combining real and estimated data to obtain these values (Rayner et al., 2003; BADC, 2010).

M = 100.566−0.718·logL∞+0.02·T (A.2)

Table A.2: Fishing mortality based on per capita consumption rate of 120kg·person·year−1.

Species group % of total catch Catches (Tonnes) Hunting Mortality Arctic Char 35 421.614 6.33E-04 Atlantic Salmon 1 12.046 3.90E-05 Gadiformes 20 240.923 2.67E-04 Sculpins/ Zoarcids 20 240.923 6.60E-04 Capelin 10 120.461 1.82E-04 Sandlance 3 36.138 6.15E-05 Sharks/Rays 0 0 0 Other Marine Fish 5 60.231 9.14E-05 Brackish Fish 2 24.092 3.97E-04

Values for natural mortality (eq. A.2), were created using ﬁsh from tropical and temperate habitats and often underestimates mortality for polar species (Pauly, 1980). Therefore, when considering all the species in group, higher values were generally chosen.

Fishing Mortality

Fishing Mortality is likely to occur on all fish species in HB, as subsistence fishing is common. Catches from commercial fishery attempts have proven to be small and financially unsustainable, therefore there are currently no commercial fisheries operating in the model area at present, with only a few brief attempts in the past (Stewart and Lockhart, 2005). The only recreational fishery that information is available for is for Arctic char from 1988-1997 through DFO harvest records (DFO, 1990, 1991, 1992, 1993, 1994,

306 A.1. Model Parameters by Functional Group

1995, 1996, 1997). Subsistence mortality was estimated using a per capita use rate derived from values provided by various sources from 1970-2001 (Anonymous, 1979; Gamble, 1988; Fabijian and Usher, 2003) for the communities of Arviat, Paulatuk, and Inukjuaq as presented in (Booth and Watts, 2007). For fish a per capita consumption rate of 30-120kg·person·year−1 was estimated for 1970. Underreporting is believed to occur, therefore the upper estimate of 120kg·person·year−1 was believed to be more accurate. Based on a population size of 10,033 (see fishing section for community population estimates) this would yield a total catch of 1204.6t of fish caught for subsistence hunting in 1970. This was divided among all fish groups except sharks and rays. Catch was divided among the different species groups based on sporadic community records of fish catches from 1975-1990 as presented in table 14-8 of Stewart and Lockhart (2005). The contributions of total catches by species group are presented in table A.2, and include the estimated hunting mortality. Consumption rates were calculated using equation J.6 from Palomares and Pauly (1998):

Q ′ log = 7.964 − 0.204 · logW∞ − 1.965T + 0.532h + 0.398d (A.3) B

where W∞ is the weight a fish would reach if it grew to it L∞ (the mean length of very old fish), T ′is the mean temperature in Kelvin, expressed as (1000/(C + 273.15)) with C representing temperature in degrees Celsius. A is the aspect ratio of the caudal fin, h and d represent variables for feeding types; h=1 if the fish is herbivorous, h=0 if it consumes other food types, d=1 if the fish is a detritivore, d=0 if the fish consumes other food types. Again a temperature of 0.5◦C was used based on the average temperature for this region. The Ecotrophic Efficiency (EE) for all fish groups was set to 0.95 in order to allow the modeling program to estimate biomass parameters. Previous modeling indicates values close to one are widely used for mid trophic level groups, indicating most of the organisms are consumed within the food web

307 A.1. Model Parameters by Functional Group or from fishing, and relatively few die from old age (Christensen et al., 2005). The value 0.95 was chosen to assume 95% of the population will die from predation and fishing mortality, a commonly used value for EE (Christensen et al., 2005). Parameters calculated for all fish species are presented in table A.3.

Arctic Charr

The Arctic Charr (Salvelinus alpinus) group consists of only one species. Charr are anadromous, living primarily in marine waters (Stewart and Lock- hart, 2005). Due to the locations and increased availability for a short time period while in HB and JB, charr are hunted by subsistence and recreational hunters (Stewart and Lockhart, 2005). Arctic charr in HB prey on amphipods, mysids, and ﬁsh (Stewart and Lockhart, 2005). In Labrador the diet consists of ﬁsh (capelin, sand lance, and various sculpins), mollusks, crustaceans, insects, and chaetognaths (Dempson et al., 2002). Diet for the model was set to: 1% Atlantic salmon, 1% gadiformes, 1% sculpins/zoarcids, 2% capelin, 2% sandlance, 2% Other Marine Fish, 2% Brackish Fish, 10% macro-zooplankton, 5% euphausiids, 31% copepods, 10% crustaceans, 10% other meso-zooplankton, 10% micro-zooplankton, 3% marine worms, 2% echinoderms, 3% other benthos, 4% primary production, 1% ice algae.

Atlantic Salmon

The Atlantic salmon group also consists of only one species Salmo salar, which utilizes the marine environment during the winter in HB, JB, and HS. Although this species is not common in HB and JB it is harvested as bycatch, and is more prevalent in the Ungava Bay area just outside of the model area (Stewart and Lockhart, 2005). Atlantic salmon is not known to be a major contributor to predator diets. Although region specific studies have not been done, in other areas juveniles prey on a range of invertebrates (mollusks, crustaceans, and small fish), while adults have been known to prey on fish (capelin, sandlance, and small cod) (Froese and Pauley, 2008). For the model the diet was set to: 1% Arctic char, 1% Atlantic salmon,

308 A.1. Model Parameters by Functional Group

Table A.3: Calculated input parameters for all ﬁsh groups within the model. NA indicates parameter could not be calculated due to missing information required for calculations.

Group Species Common L∞ Average Mortality M at Q/B ◦ Name Temp at Av- 0.5 C at ◦ C erage 0.5 ◦ Temp C Arctic Char Salvelinus alpinus Arctic Char 1.5 0.1 0.1 1.7 Atlantic Salmo salar Atlantic 156 9 0.3 0.25 7.14 Salmon Salmon Gadiformes Arctogadus Polar cod 34 8 0.55 0.46 2.3 glacialis Boreogadus saida Arctic cod 31.3 1 0.31 0.3 2.5 Gadus ogac Greenland cod 79.5 1 0.22 0.22 1.3 Sculpins/ Gymnocanthus Arctic 31.5 1 0.3 0.29 2.2 Zoarcids tricuspis staghorn Icelus bicornis twohorn 16.6 1 0.51 0.5 3.6 sculpin Icelus spatula spatulate 22.1 3 0.35 0.33 3 sculpin Myoxocephalus fourhorn 33.1 1 0.32 0.32 2.1 quadricornis sculpin Myoxocephalus Arctic sculpin 23.2 1 0.32 0.39 2.9 scorpioides Myoxocephalus shorthorn 21.9 9.3 0.79 0.64 2.7 scorpius sculpin Triglops murrayi moustache 21.1 10 0.65 0.42 3.1 sculpin Triglops pingelli ribbed sculpin 27.3 10 0.35 0.28 3 Gymnelus viridis fish doctor 58.1 1 0.28 0.28 1.6 Lycodes pallidus pale eelpout 27.3 1 0.41 0.35 2.6 Lycodes reticula- Arctic eelpout 37.6 1.3 0.3 0.28 2.2 tus Capelin Mallotus villosus capelin 16.9 4.3 0.85 0.78 3.9 Sandlance Ammodytes du- northern sand 26.2 2 0.45 0.44 3.8 bius lance Ammodytes stout sand 31.5 10 0.47 0.38 2.4 hexapterus lance Sharks/Rays Somniosidae sleeper sharks 0.04 0.5 Rajidae skates 0.18 2 Other Ma- Leptagonus alligator 22.1 1 0.45 0.41 3 rine Fish decagonus poacher Ulcina olriki Atlantic alliga- 9.2 1 1.03 0.77 5.3 torfish Cyclopterus lum- lumpfish 55 5 0.19 0.17 1.3 pus Eumicrotremus leatherfin NA 4.7 derjugini lumpsucker Eumicrotremus Atlantic spiny NA 4 spinosus lumpsucker Careproctus rein- sea tadpole 31.5 3 0.57 0.32 2.4 hardti Liparis fabricii gelatinous 21.1 8 0.94 0.42 3.1 snailfish Liparis gibbus dusky snailfish 54 1 0.33 0.21 1.7 Liparis tunicatus kelp snailfish 16.9 1 0.98 0.49 3.5 Anisarchus stout eelblenny 31.5 1 0.26 0.32 2.4 medius Eumesogrammus fourline snake- 23.2 1 0.35 0.39 2.9 praecisus blenny Leptoclinus macu- daubed shanny 21.1 1 0.38 0.42 3.1 latus Pholis fasciata banded gunnel 31.5 1 0.49 0.32 2.4 Clupea harengus Atlantic Her- 30.4 9 0.48 0.39 10.1 ring Brackish Lumpenus fabricii slender eel- 38.1 1 0.28 0.28 2.2 Fish blenny Stichaeus puncta- Arctic shanny 14.5 12 0.94 0.55 3.9 tus Hippoglossoides Canadian 70.4 1.4 0.19 0.18 1.7 platessoides plaice

309 A.1. Model Parameters by Functional Group

2% gadiformes, 2% sculpins/zoarcids, 5% capelin, 2% sandlance, 2% other marine ﬁsh, 3% brackish ﬁsh, 5% cephalopods, 15% macro-zooplankton, 8% euphausiids, 8% copepods, 18% crustaceans, 3% other meso-zooplankton, 15% micro-zooplankton, 7% primary production, and 3% ice algae.

Gadiformes

The Gadiformes group includes Arctic cod (Boreogadus saida), Greenland cod (Gadus ogac), and Polar cod (Arctogadus glacialis). These ﬁsh are important to the diets of many marine mammals in the area (see narwhal, ringed seal, harp seal, and beluga sections), although Arctic and Polar cod are more important to higher predators than Greenland cod. Arctic cod are believed to be declining, as their presence in the diet of thick-billed murres has declined since the 1980s (Gaston et al., 2003). Greenland cod in northern HB are omnivorous feeding primarily on benthic species; crabs, amphipods, polychaetes, and crustaceans, with few species consuming them, while Arctic cod take mostly copepods, hyperiid amphipods, ice-associated crustacea, and other pelagic prey, and are more important to higher predators than Greenland cod (Mikhail and Welch, 1989). The diet for this group was set to 2% gadiformes, 5% capelin, 5% sandlance, 6% other marine ﬁsh, 3% crustaceans, 15% marine worms, 15% bivalves, 20% other benthos, 10% ice algae, and 4% ice detritus.

Sculpins/Zoarcids

Sculpins (Family: Cottidae) and zoarcids or eelpouts (Family: Zoarcidae) were combined to form one functional group and include: Arctic eelpout (Lycodes reticulates), Arctic sculpin (Myoxocephalus scorpioides), Arctic staghorn (Gymnocanthus tricuspis), ﬁsh doctor (Gymnelus viridis), fourhorn sculpin (Myoxocephalus quadricornis), moustache sculpin (Triglops murrayi), pale eelpout (Lycodes pallidus), ribbed sculpin (Triglops pingelli), shorthorn sculpin (Myoxocephalus scorpius), spatulate sculpin (Icelus spatula), and twohorn sculpin (Icelus bicornis). These two families were combined as nearly all members are small benthic ﬁsh found in shallow, mostly

310 A.1. Model Parameters by Functional Group coastal waters. Of the eelpout species, only the fish doctor has been noted as important to predators, namely cods and sculpins, while the importance of pale and Arctic eelpouts are unknown (Stewart and Lockhart, 2005). However, sculpins are consumed by cods, seabirds, seals, and other marine mammals, in addition to being caught for sport fishing occasionally (Stewart and Lockhart, 2005). The diets of these fish include plant materials, aquatic insects, crustaceans, benthic amphipods, polychaetes, bivalves, and detritus (Froese and Pauley, 2008). The diet was set to 2% sculpins/zoarcids, 5% capelin, 5% sandlance, 4% other marine fish, 7% crustaceans, 15% marine worms, 11% echinoderms, 15% bivalves, 20% other benthos, 6% ice algae, and 10% ice detritus.

Capelin

Capelin (Mallotus villosus) is a marine species with a circumpolar distribution in the Arctic, sometimes occurring in brackish or freshwater, and is often found in schools (Froese and Pauley, 2008). The population in HB is believed to be a surviving remainder from a warmer time period, likely the 1880s or earlier, with large swarms occurring in southern HB (Dunbar, 1983). The ecology of adult capelin in HB is not well known (Stewart and Lockhart, 2005), although they have been shown to be an important prey item to belugas, harp seals, and many bird species (Beck et al., 1993; Gas- ton et al., 2003; Loseto et al., 2009). Changes to the diets of thick-billed murres have identiﬁed a possible increase in capelin from 1980-2002 for birds located in the northern portion of HB (Gaston et al., 2003). The general diet of capelin is based on planktonic crustaceans, copepods, euphausiids, amphipods, marine worms, and small ﬁshes (Froese and Pauley, 2008). For the model the diet was set to 15% macro-zooplankton, 20% euphausiids, 20% copepods, 10% crustaceans, 5% other meso-zooplankton, 10% micro- zooplankton, 15% pelagic production, and 5% pelagic detritus.

311 A.1. Model Parameters by Functional Group

Sandlance

The sandlance group contains two species the northern sand lance (Am- modytes dubius) and the stout sand lance (Ammodytes hexapterus). Both species are small bottom dwelling ﬁsh which burrow in the sand and are important in the diets of forage ﬁsh, seabirds, and marine mammals (Stewart and Lockhart, 2005). Sandlance feed on zooplankton, primarily copepods, crustaceans, and worms (Froese and Pauley, 2008). The diet was set to 2% cephalopods, 5% macro-zooplankton, 15% euphausiids, 35% copepods, 5% crustaceans, 10% other meso-zooplankton, 15% micro-zooplankton 10% pelagic production, and 3% pelagic detritus.

Sharks/Rays

The Greenland shark (Somniosus microcephalus) and the thorny skate (Am- blyraja radiate) are both bottom dwelling and likely very uncommon in HB and JB. The Greenland shark has been suggested to be present in HB, and the thorny skate is only noted to be found in James Bay, within the model area (Stewart and Lockhart, 2005). Both are probably rare in the area, and not likely to be a significant contribution to fish biomass in general. Skates consume small fish and benthic invertebrates, while the Green- land shark consumes fish, seals, whales, and birds (Stewart and Lockhart, 2005; Froese and Pauley, 2008). The diet was set to 1% narwhal, 1% bearded seal, 1% ringed seal, 1% harp seal, 5% Arctic char, 2% Atlantic salmon, 15% gadiformes, 15% sculpins/zoarcids, 5% capelin, 8% sandlance, 1% sharks/rays, 6% other marine fish, 4% brackish fish, 5% cephalopods, 5% macro-zooplankton, 1% euphausiids, 5% crustaceans, 5% marine worms, 10% echinoderms, 1% bivalves, and 3% other benthos.

Other Marine Fish

The other marine ﬁsh group includes herring (family: Clupeidae), poach- ers (family: Agonidae), lumpﬁshs (family: Cyclopteridae), shannies (family: Stichaeidae), and gunnels (family: Pholidae), Species include: alligator

312 A.1. Model Parameters by Functional Group poacher (Leptagonus decagonus), Atlantic alligatorfish (Ulcina olriki), At- lantic Herring (Clupea harengus), Atlantic spiny lumpsucker (Eumicrotremus spinosus), banded gunnel (Pholis fasciata), daubed shanny (Leptoclinus maculatus), dusky snailfish (Liparis gibbus), fourline snakeblenny (Eume- sogrammus praecisus), gelatinous snailfish (Liparis fabricii), kelp snailfish (Liparis tunicatus), leatherfin lumpsucker (Eumicrotremus derjugini), lumpfish (Cyclopterus lumpus), sea tadpole (Careproctus reinhardti), and stout eelblenny (Anisarchus medius). These fish are all small benthic fish that live near varied substratum (mud, sand, and rocks), with the exception of herring, which are predominantly pelagic and schooling living from the surface to 200m. These fish are prey items for cod, seabirds, seals, other fish and lumpfish are noted to be eaten by Greenland sharks (Stewart and Lockhart, 2005). Diets of these fish are focused on benthic and pelagic invertebrates, primarily crustaceans, polychaetes, clams, fish eggs, zooplankton, and herring have the ability to filter feed (Froese and Pauley, 2008). The diet was set to 2% capelin, 1% cephalopods, 5% macro-zooplankton, 2% euphausiids, 20% copepods, 20% crustaceans, 2% other meso-zooplankton, 5% micro-zooplankton, 6% marine worms, 5% bivalves, 5% other benthos, 10% pelagic production, 10% ice algae, and 7% pelagic detritus.

Brackish Water Fish

The brackish water group includes two species of shannies (family: Stichaei- dae) which were considered to be brackish based; Arctic shanny (Stichaeus punctatus) and the slender eelblenny (Lumpenus fabricii) and one righteye flounder (family: Pleuronectidae), Canadian plaice (Hippoglossoides platessoides). Although all three of these species are found in inshore waters, they have been classified as brackish rather than marine and are consumed by larger marine fish and seabirds (Stewart and Lockhart, 2005). The diets consist of invertebrates; crustacean, worms, and clams, in addition to small fish and fish eggs (Froese and Pauley, 2008). The diet was set to 2% capelin, 2% sandlance, 2% brackish fish, 2% cephalopods, 17% macro-zooplankton, 5%

313 A.1. Model Parameters by Functional Group euphausiids, 5% copepods, 15% crustaceans, 5% other meso-zooplankton, 20% other meso-zooplankton, 2% marine worms, 2% echinoderms, 6% other benthos, 9% pelagic production, 1% ice algae, and 5% pelagic detritus.

Zooplankton

Sampling of zooplankton has occurred twice in the HB region, once with a survey by Harvey et al. (2001) to sample the eastern side of HB in 1993, starting in JB and moving northward up the coast and into Hudson Strait. The second survey conducted in 2003 spanned from west to east just above 60◦N latitude (Harvey et al., 2006). Results from the surveys indicate higher zooplankton biomass on the western side compared to the eastern side of Hudson Bay, and increasing concentration as samples increased in latitude from James Bay up into Hudson Strait. From the 1993 south to north survey (Harvey et al., 2001), biomass of samples ranged from 2.6 to 28.1g·m2. Original samples were presented in dry weight (0.52 to 5.62 g ·m2), but converted to wet weight using a conversion factor of 5 (DW:WW) for zooplankton (Cushing et al., 1958; Cauf- fope and Heymans, 2005). Samples were dominated by copepods, euphausiids, cnidarians, amphipods, and chaetognaths indicating sampling of the meso and macro-zooplankton (chaetognaths fall into the macro-zooplankton, while most other species are smaller and fall into the meso-zooplankton spec- trum). The 2003 east to west survey (Harvey et al., 2006) identiﬁed meso- zooplankton, dominated by copepods, to have 3 times more biomass than macro-zooplankton in Hudson Bay. This ratio was higher in Hudson Strait and Foxe Basin, up to 10 times more meso-zooplankton. Of the zooplankton standing stock 5-17% of the abundance of zooplankton sampled was macro- zooplankton for the HB portion with the chaetognaths Sagitta elegans as the most abundant. Wet weight of macro- and meso-zooplankton ranged from 5-10g·m2 for HB samples, although biomasses were higher for Hudson Strait, up to 20g·m2 for macro-zooplankton, and 110g·m2 for meso-zooplankton.

314 A.1. Model Parameters by Functional Group

Cephalopods

While little is known about cephalopods in HB, they appear in the diets of predators; birds, seals, and some whale species. Gonatus fabricii is an important prey item in the diets of thick-billed murres, and is the only species recorded within the model area (Gerdiner and Dick, 2010). However, Rossia moelleri and other unidentiﬁed cephalopods have been recorded just outside the model area (Gerdiner and Dick, 2010) indicating a strong possibility more than one species is found within HB. This combined with the diets of predators led to the belief cephalopods are present within the model area, and were therefore included as a functional group. The biomass for cephalopods was estimated by the model given other parameters. The P/B and Q/B of 2.55 and 6.9y−1 were taken from the cephalopod group in the 1979 Aleutian Island model (Heymans, 2005). How- ever, these values were adjusted in the balancing of the model to 1.5 and 5y−1 for P/B and Q/B. The EE for this group was set to 0.95. Diet for cephalopods was set to 1% Arctic char, 1% Atlantic salmon, 5% gadiformes, 5% sculpins/zoarcids, 8% capelin, 8% sandlance, 1% other marine ﬁsh, 4% cephalopods, 18% macro-zooplankton, 4% euphausiids, 13% copepods, 10% crustaceans, 10% other meso-zooplankton, and 12% micro-zooplankton based on the diet of Antarctic cephalopods (Rodhouse and White, 1995; Jackson et al., 2002).

Macro-Zooplankton

The macro-zooplankton group includes all zooplankton species larger than 2mm. Chaetognaths (sagita elegans) were the most abundant species from sample taken in eastern HB in 1993, with hydromedusa (Aeginopsis lau- rentii) being the second most abundant, and numerous unidentiﬁed species (Harvey et al., 2006). Biomasses from the 2003 survey were reported between 5-10g·m2. A value of 7.5g·m2 or t·km2 was used for the biomass. P/B values of zooplankton larger than 1 mg WW for the Prince William Sound model ranged from 0.1 to 1.5y−1 depending on the season, and Q/B ratios ranged from 0.33 to 5y−1 (Okey and Pauly, 1999). P/B for HB was set to

315 A.1. Model Parameters by Functional Group

1y−1 and Q/B set to 3y−1 based on the values from Prince William Sound. Chaetognaths were the most abundant species in this group, with a diet focused on copepods (Tonnesson and Tiselius, 2005). Other members of this group were believed to prey upon smaller zooplankton and phytoplankton species. The diet was set to 6.5% euphausiids, 19% copepods, 2% crustaceans, 5% other meso-zooplankton, 30% micro-zooplankton, 22% pelagic production, 10.5% ice algae, and 5% pelagic detritus.

Euphausiids

Euphausiids show increasing contribution to the meso-zooplankton biomass moving south to north (Harvey et al., 2001). euphausiids consisted of Thysanhoessa rachii and other unidentiﬁed species. Based on the 1993 samples euphausiids contributed on average 2.14g·m2 or t·km2 to the zooplankton biomass. The P/B for this group was set to 3y−1 based on a krill larva value of 4, and adult krill value of 1 from the Antarctic Peninsula ecosystem model (Efran and Pitcher, 2005). A P/Q ratio of 0.25 was assumed (Chris- tensen et al., 2005), to allow the model to estimate both EE and Q/B. The diet was set to 1% macro-zooplankton, 0.1% euphausiids, 55.9% copepods, 1% crustaceans, 5% other meso-zooplankton, 10% micro-zooplankton. 15% pelagic production, 8% ice algae, and 4% pelagic detritus based on the diet of Antarctic euphausiids (Pakhomov et al., 1997; Cripps and Atkinson, 2000; Atkinson et al., 2002).

Copepods

Small copepods dominate the mesozooplankton biomass, up to 82% of total zooplankton biomass at on station in northern HB (Harvey et al., 2001). The average biomass over all stations sampled in 1993 was 4.015g·m2 or t·km2, and thus was the biomass used for the model. Species include: Acartia longiremis, Calanus glacialis, Calanus ﬁnmarchicus, Calanus hyperboreus, Centropages hamatus, Metridia longa, and Pseudocalanus spp. as well as other unidentiﬁed species. P/B for the Prince William Sounds model copepod group was 5y−1 (Okey and Pauly, 1999). Other zooplankton groups

316 A.1. Model Parameters by Functional Group show higher P/B values ranging from 5.8 to 36.3y−1 for the Aleutian Is- lands (Heymans, 2005) or 10.7 to 24y−1 for the Kerguelen Islands (Pruvost et al., 2005). A P/B of 16y−1 was used for the HB model. A P/Q of 0.25 was assumed to give a Q/B value of 64y−1 when balancing the model. Cope- pods are primarily grazers, with a strong link to ice algae identiﬁed in HB (Runge and Ingram, 1987, 1991). Copepods have also been noted to consume other zooplankton species (Metz and Schnack-Schiel, 1995). The diet was set to 5% micro-zooplankton, 70% pelagic production, 20% ice algae, and 5% pelagic detritus.

Crustaceans

The crustacean group includes all benthic crustaceans and zooplankton crustaceans (with the exception of euphausiids and copepods). The benthic and planktonic species were combined due to lack of distinction in the diet for higher predators. For the planktonic species this includes various Isopoda, Ostracoda, Amphipoda, Decapoda, and Cirripedia. Biomass for the planktonic component was averaged to 1.05g·m2 based on the 1993 survey. For the benthic component more species were identiﬁed (147 species compared to 5 identiﬁed for pelagic with many unknown) from Amphipoda, Cirri- pedia, Cumacea, Decapoda, Isopoda, Nebaliacea, Ostracoda, Pycnogonida, and Tanaidacea. In the Weddell Sea benthic Crustacea and Chelicerata contribute 0.45g·m2or t·km2 (Jarre-Teichmann et al., 1997). Although the contribution of benthic crustaceans is known in this area, it was estimated to be no more than the planktonic component. A biomass of 1.8g·m2 was used for the model. P/B for various crustacean plankton for Prince William Sound ranged from 2-8y−1 (Okey and Pauly, 1999). P/B for benthos ranged from 0.7y−1 for benthic crustaceans in the Weddell Sea (Jarre-Teichmann et al., 1997) to 2.1y−1 for benthic invertebrates for the Aleutian Islands (Heymans, 2005). A P/B value of 3.6y−1 was used along with a P/Q ratio of 0.25 to give a Q/B ratio of 14.4y−1. Antarctic amphipod diet consists primarily of detritus with some polychaetes, crustaceans, echinoderms and bryozoans (Dauby et al., 2001). In

317 A.1. Model Parameters by Functional Group

HB amphipods can signiﬁcantly reduce the inshore algal biomass suggesting their ability to consume large amounts of producers (Stewart and Lock- hart, 2005). Benthic crustaceans were assumed to be primarily scavengers and carnivores. The diet was set to 1% euphausiids, 5% copepods, 0.5% crustaceans, 1% other meso-zooplankton, 1% micro-zooplankton, 5% marine worms, 5% echinoderms, 5% bivalves, 10% other benthos, 30% pelagic production, 16.5% ice algae, 10% ice detritus, and 10% pelagic detritus.

Other Meso-Zooplankton

The other meso-zooplankton group includes numerous unidentiﬁed species from the phyla Cnidarians, Annelida, Mollusca, and Urochordata. The average biomass for this group based on the 1993 survey was 1.21g·m2. The P/B was set to 10y−1 based on overall zooplankton averages from the Prince William Sound model (Okey and Pauly, 1999). The P/Q was set to 0.25 to give a Q/B of 40y−1. Global analysis of meso-zooplankton consumption on primary producers indicated that in less productive marine systems meso-zooplankton were more reliant on alternative food sources such as protozoans and other zooplankton (Calbert, 2001). For the HB region the diet was assumed to be 5% euphausiids, 10% copepods, 2% crustaceans, 1% other meso-zooplankton, 10% micro-zooplankton, 45% pelagic production, 22% ice algae, and 5% pelagic detritus.

Micro-Zooplankton

The micro-zooplankton group includes all zooplankton smaller than 0.2mm. Sampling is not likely to include these smaller species as the mesh size in the nets is expected to let the smaller plankton through. Therefore there are no estimates of biomass for this group. Other model values for small zooplankton in the Aleutian Islands show a P/B ratio of 36y−1 and a Q/B of 112y−1 (Heymans, 2005). Herbivorous zooplankton from the Kerguelen Islands were estimated to have a P/B of 24y−1 and a Q/B of 96y−1 (Pru- vost et al., 2005). Okey and Pauly (1999) state a P/B of 15y−1 for small zooplankton in Prince William Sound. For the HB model the P/B was set

318 A.1. Model Parameters by Functional Group to the lower range of 15y−1 and a Q/B of 45y−1 was assumed. The EE for this group was set to 0.95. As micro-zooplankton are primarily grazers, although they have been noted to consume detritus in addition to ice algae in the winter months (Bathmann et al., 1993). The diet was set to 75% pelagic production, 17% ice algae, and 8% pelagic detritus.

Benthos

There are few benthic species in the intertidal zone, however, below the sea ice the most common invertebrates are echinoderms, sea spiders, polychaetes, sea spiders, and worms (Stewart and Lockhart, 2005). Various surveys of HB from 1953-1967 (Atkinsor and Wacasey, 1989) identify presence of certain benthic species, however they fail to indicate abundance. From this survey there were 76 species of annelids identiﬁed, 157 arthropods, 53 cnidarians, 83 molluscs, 1 nemertean, 4 porifera, and 4 sipunculans. For each location species were recorded indicating which groups were present at the most locations. Benthos were split into four groups: marine worms, echinoderms, bivalves, and other benthos, primarily based on the diets of higher trophic level groups and their diets. Due to the lack of information for these species groups, parameters from other models of similar regions were incorporated and used for the benthic species. Parameter values of benthic invertebrates for other high latitude regions (Gulf of Alaska, Kerguelen Islands, and the Weddell Sea) are presented in table A.4. Of the models built for higher latitudes, the Weddell Sea model is most comparable to the HB region, as the Gulf of Alaska and Kerguelen Islands are more open, productive ecosystems, while the Weddell Sea has less mixing compared to the other two. Brey and Gerdes (1998) found community P/B ratio to increase from 0.18y−1 to 0.55y−1 as depth increases for the Weddell and Lazarev Seas (Antarctica). For all benthic groups biomass was estimated, using inputs for P/B, Q/B, and a value of 0.95 for the ecotrophic eﬃciency.

319 Table A.4: Comparison of parameters for benthic functional groups from high latitude Ecopath models. Biomass (B) is presented in t · km−2, production to biomass ratio (P/B) and consumption to biomass ratio (Q/B) are presented as an annual rate y−1. NA indicates value was not available

Functional Group Model Area Model B P/B Q/B Reference Year

Epibenthic Carnivores Gulf of Alaska 1963 35.601 2 17 Heymans (2005) Benthic Invertebrates Gulf of Alaska 1963 5.194 0.98 6.553 Heymans (2005) Deep benthic omnivores Kerguelen Is. 1987 30 3 10 Pruvost et al. (2005) Shallow benthic omnivores Kerguelen Is. 1987 3.1 2.1 10 Pruvost et al. (2005) Shallow benthic carnivores Kerguelen Is. 1987 8.7 2 10 Pruvost et al. (2005) benthic mollusca Weddell Sea 1980s NA 0.3 1 Jarre-Teichmann et al. (1997) Tunicata Weddell Sea 1980s 2.8 0.3 1 Jarre-Teichmann et al. (1997) Porifera Weddell Sea 1980s 4.81 0.18 0.6 Jarre-Teichmann et al. (1997) Hemichordata Weddell Sea 1980s 6.26 0.3 2 Jarre-Teichmann et al. (1997) Lophophora and Cnidaria Weddell Sea 1980s 7.49 0.1 1 Jarre-Teichmann et al. (1997) Benthic Crustacea and Che- Weddell Sea 1980s 0.45 0.7 3.5 Jarre-Teichmann et al. (1997) licerata Polychaeta and other worms Weddell Sea 1980s 27.51 0.6 4 Jarre-Teichmann et al. (1997) Echinoidea Weddell Sea 1980s 0.54 0.07 0.233 Jarre-Teichmann et al. (1997) Crinoidea Weddell Sea 1980s 6.2 0.3 1 Jarre-Teichmann et al. (1997) Ophiuroidea Weddell Sea 1980s 24 0.173 0.577 Jarre-Teichmann et al. (1997) Asteroidea Weddell Sea 1980s 20.88 0.08 0.267 Jarre-Teichmann et al. (1997) Holothuroidea Weddell Sea 1980s NA 0.2 1.1 Jarre-Teichmann et al. (1997) Large Crabs Newfoundland 1995-1997 0.232 0.3 1.2 Heymans (2003) Small Crabs Newfoundland 1995-1997 1.942 0.3 1.5 Heymans (2003) Lobster Newfoundland 1995-1997 0.003 0.38 4.42 Heymans (2003) Shrimp Newfoundland 1995-1997 1.859 1.45 9.667 Heymans (2003) Echinoderms Newfoundland 1995-1997 112.3 0.6 6.667 Heymans (2003) Polychaetes Newfoundland 1995-1997 10.5 2 22.222 Heymans (2003) Bivalves Newfoundland 1995-1997 42.1 0.57 6.333 Heymans (2003) Other Benthic Invertebrates Newfoundland 1995-1997 7.8 2.5 12.5 Heymans (2003) 320 A.1. Model Parameters by Functional Group

Marine Worms

The marine worm functional group includes all phyla of worms; Nematoda (round worms), Phoronida (horseshoe worms), Priapulida (priapulid or penis worms), Sipuncula (peanut worms), and Annelida (bristle worms). P/B and Q/B values of 0.6 and 4y−1 respectively, were used based on the Weddell Sea model for the group ”polychaetes and other worms” (Jarre-Teichmann et al., 1997), along with an EE of 0.95. Feeding types range from deposit feeders (Polychaetes) to trap feeders (Sipunculans) (Por and Bromley, 1974; Brock and Miller, 1999). The diet was set to 1% macro-zooplankton, 1% euphausiids, 3% copepods, 1% crustaceans, 2% other meso-zooplankton, 3% micro-zooplankton, 1% marine worms, 1% echinoderms, 10% other benthos, 4% pelagic production, 12% ice algae, and 61% ice detritus.

Echinoderms

The echinoderm functional group contains all species under the phylum Echinodermata, which includes the following classes: Asteroidea (Sea stars), Crinoidea (sea lillies), Echinoidea (sea urchins), Holothuroidea (sea cucumbers), and Ophuiroidea (brittle stars). The P/B and Q/B ratios were taken from all echinoderm groups in the Weddell Sea model and averaged to give 0.164 and 0.63y−1 (Jarre-Teichmann et al., 1997). However, these values were too low to balance the model, so they were increased to 0.3 and 1y−1 (P/B and Q/B) to the higher limits of this phylum for the Weddell Sea model (as the values for Crinoidea) to balance the model. The diet was set to 1% euphausiids, 2% copepods, 5% crustaceans, 1% other meso-zooplankton, 3% micro-zooplankton, 10% marine worms, 1% echinoderms, 10% bivalves, 15% other benthos, 3% pelagic production, 8% ice algae, and 41% ice detritus to account for a range of feeding modes. Sessile echinoderms rely on suspended particles, while more active echinoderms such as seastars are able to actively hunt prey and most likely feed on other benthic species in the region.

321 A.1. Model Parameters by Functional Group

Bivalves

HB bivalves are from the class Pelecypoda (phylum Mollusca). This class was given its own functional group due to its importance to walrus, bearded seals, and ﬁsh. The P/B and Q/B values of 0.57y−1 and 6.33y−1 were taken from the Newfoundland model (Heymans, 2003) and used for the HB values. The EE was set to 0.95 and the biomass was estimated by the model. As suspension feeders, bivalves were assumed to prey on species likely to come in contact with them. The diet was set to 3% copepods, 5% other meso- zooplankton, 5% micro-zooplankton, 5% pelagic production, 12% ice algae, and 70% ice detritus.

Other Benthos

The other benthos group includes all other invertebrate species found within HB. Those which have been named by Atkinsor and Wacasey (1989) include molluscs (Scaphopods or tusk shells), porifera (sponges), Pycnogo- nida (Arthropod: sea spiders), Ascidiacea (sea squirts), Brachiopoda (lamp shells), Cnidarians; anthozoa and hydrozoa (anemones/corals and hydroids), Bryozoa (moss animals). Based on the benthic invertebrate groups from the Gulf of Alaska (Heymans, 2005), the shallow benthic omnivores from the Kerguelen Islands (Pruvost et al., 2005), and the other benthic invertebrates from Newfoundland (Heymans, 2003), the P/B was set to 2.5y−1, and the Q/B was set to 12.5y−1. The EE was set to 0.95 and the biomass was estimated for this group. A general diet was set to 1% macro-zooplankton, 1% other meso-zooplankton, 1% micro-zooplankton, 1% marine worms, 1% echinoderms, 1% bivalves, 1% other benthos, 5% pelagic production, 22% ice algae, and 66% ice detritus, as there are a variety of feeding types in this group.

Primary Production

Primary production in the model was split into two groups; pelagic production and ice algae. Pelagic production refers to the producers which bloom in the springtime in a seasonal pulse and are not generally available to the food

322 A.1. Model Parameters by Functional Group web the remainder of the year. The ice algae group represents the species which are frozen into the sea ice in the fall and are released when the sea ice melts. Many of the species frozen within the ice are accessible throughout the winter via brine channels in the ice. Numerous species of producers exist including: dinoflagellates, Prasinophytes, cryptophytes, chryophytes, centric diatoms, chlorophytes, flagellates, Prymnesiophytes, and pennate diatoms (Harvey et al., 1997). Two surveys of phytoplankton have been completed in HB; one in 1993 sampling from James Bay up the east coast of Hudson Bay into Hudson Strait (Harvey et al., 1997), and a second in 2003 running east to west through the middle of HB (Harvey et al., 2006). The first survey in 1993 yielded estimates of 0.36-133.5t·km2 (based on chl a samples of 1.2-145mg·m2), and the second survey estimated 7.5-75t·km2 (based on chl a samples of 25-250mg·m2)15.

Pelagic Production

Pelagic production was sampled at 0.33-129t·km2 (1.1-431mgchla · m2) in 1993 (Harvey et al., 1997), although this was during the ice free season, so a biomass of 8t·km2 was assumed as the starting value.The EE was set to 0.8 to represent a 20% sinking rate to detritus, and the P/B ratio was estimated by the model.

Ice Algae

The ice algae contribution to primary production was sampled to be 0.03- 4.2t·km2 (0.1-14mgchla · m2) in 1993 (Harvey et al., 1997), 0.003-6t·km2 for values ranging 1978-1990 (Legendre et al., 1996), and 0.03-3.6t·km2 in 1986 (Tremblay et al., 1989). The contribution is thought to be slightly higher at the start of the model in 1970, as the extent of sea ice has decreased since this time. Ice algal contribution to total production has been estimated at 25% in Hudson Bay (Legendre et al., 1996) and ranging from to 57% of

15Wet weight (t·km2) was calculated using the conversionchla =1.5% of ash free dry weight (AFDW)(Farabee, 2001), 1g carbon=2g AFDW (Cauﬀope and Heymans, 2005), and 1g C=9g wet weight (WW) (Pauly and Christensen, 1995)

323 A.1. Model Parameters by Functional Group all production in the central Arctic to 3% in surrounding sub-Arctic areas (Gosselin et al., 1997). Biomass of algae within the ice has reached levels of 0.6gC·m2 in the Antarctic (Weddell Sea and Antarctic Peninsula) during spring and fall in the 1980s (Garrison and Buck, 1989), giving a biomass of 5.4·km2 (Pauly and Christensen, 1995)16. The biomass for HB was set to 3.5t·km2. The EE for ice algae was set to 0.65, to account for the export of producers from the ice algae to ice detritus. Based on Tremblay et al. (1989), at least 20% of ice algal production during the spring was exported to the benthos, with 30% remaining in the pelagic zone, and another 50% thought to remain in the water column. As a yearly average, it was assumed that 45% of ice algae was exported to the ice detritus group, resulting in an EE of 0.65. The P/B was estimated by the model.

Detritus

Detrital biomasses was calculated using equation A.4 (Pauly et al., 1993):

Log10D = −2.41 + 0.954Log10PP + 0.863Log10E (A.4)

where D is the standing stock of detritus (in gC · m−2 · y−1), PP is primary productivity (in gC · m−2 · y−1), and E is the euphotic depth (in meters).

Ice Detritus

In April the maximum ice thickness is 1.5m with over 85% of HB being covered in sea ice (Danielson, 1971). To calculate ice detritus an average euphotic depth of ice algae was assume to be 0.5m, combined with the ice algae biomass gave a ice detritus biomass of 0.009t·km2.

16Using the conversion for phytoplankton where 1g C=1g wet weight

324 A.2. Fisheries Input

Pelagic Detritus

For the pelagic detritus group, a euphotic depth of 50m was used (Harvey et al., 1997), to give a value of 0.33t·km−2.

A.2 Fisheries Input

In order to incorporate hunting and fishing pressure on various species, numerous ”fisheries” were created within the model to account for catches within the first year (1970), which were then continued through the temporal simulations in Ecosim. Catches for the first year, and subsequent years are presented.

Western Hudson Bay Polar Bears

The average catch for the 1980s for WHB bears was 44 (Lee and Taylor, 1994), and then increased to an average of 46.8 bears from 1999-2004 (Aars et al., 2005). Catches were set to 44 from 1970-1998, 46.8 from 1999, and then 47 from 2005-2010 based on the 2005 quota of 47 (Aars et al., 2005). Initial catch for 1970 was set to 44 bears.

Southern Hudson Bay Polar Bears

The average catch of SHB polar bears for the 1980s was 68 (Lee and Taylor, 1994) and decreased to an average of 40.4 from 1999-2004 (Aars et al., 2005). Catch was assumed to be 68 bears per year from 1970-1990, and then decreased to 40.4 from 1991-2004. The annual quota in 2005 was set to 25 bears (Aars et al., 2005)). Catches from 2005-2010 were set to 25 bears. For 1970 the catch was set to 68 bears.

Foxe Basin Polar Bears

Average catches for the 1980s were 142 bears (Lee and Taylor, 1994) and decreased to an average of 97.4 for 1999-2004 (Aars et al., 2005). Catches were assumed to be 142 bears from 1970 to 1990, and then 97.4 bears each

325 A.2. Fisheries Input year until 2005, where the quota was raised from 97 to 106. It was assumed 106 bears were harvested each year from 2005-2010. For modeling purposes, these values were reduced to 20% to reﬂect the adjustments in biomass regarding the population size within the model area, as 20% of the Foxe Basin population resides in the model area. For 1970 the catch was set to 28.4 bears.

Killer Whale Hunting

Table A.5: Known killer whales harvests in the eastern Canadian Arctic from Higdon (2007)

Year Number of Whales Harvested 1978 1 1981 12 1995 1 2000 5

Killer whales are not generally targeted, however they are occasionally hunted in HB (Ferguson pers. comm.). Table A.5 identiﬁes known harvests of killer whales in the eastern Canadian Arctic (Higdon, 2007). These values were used for the HB killer whale population as relative catches. For 1970, there were no reported catches, however a value was needed in the model. 1 The equivalent biomass of 4 of a whale was used as a starting value.

Narwhal Hunting

In general narwhals are hunted during their migration and through the summer months primarily by Repulse Bay, with some involvement from other communities; Chesterfiled Inlet, Coral Harbour, Ranklin Inlet, Whale Cove, and Cape Dorset (DFO, 1998; Westdal et al., 2010). The annual quota for the communities within HB is currently listed at 112 whales per year. Catches of narwhal by Repulse Bay are shown in figure A.1, not including the struck and loss rate. Catches for 1970 were set to 6 whales, the same value for 1978, which is the first year there were any recorded catches. Re- ported struck and loss rates range from of 40% of total catch (Roberge and

326 A.2. Fisheries Input

Figure A.1: Reported catches of narwhal from 1977-2007 for Repulse Bay, Chesterﬁled Inlet, Coral Harbour, Ranklin Inlet, Whale Cove, and Cape Dorset (DFO, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997; Stewart and Lockhart, 2005). Figure does not incorporate a struck and loss rate.

Dunn, 1990), to 12-56%, with specific hunts up to 71% (Weaver and Walker, 1988) as observed by the Department of Fisheries and Oceans (DFO). How- ever, non-DFO observers of the hunt have commented on how hunters only take sure shots when being officially observed (Nicklen, 2007), meaning more whales are likely struck than the DFO statistics imply. The struck and loss term generally only accounts for whales known to die. Superficially wounded whales are not included in these estimates, even though they may not survive. Records also do not account for unreported catches. Although the biomass was adjusted to 50% to account for half of the year (and feeding) to occur within the model area, as the catch data excludes the struck and loss rates, and underreporting. Catches were taken as is, without adjusting for the reduced biomass in the model, indicating mortality from catches is double than reported in figure A.1.

Bowhead Hunting

There have been 6 recorded kills of bowhead whales from the HB region; 1994 (unlicensed- Foxe Basin), 1996 (Repulse Bay), 1998 (Cumberland Sound),

327 A.2. Fisheries Input

2000 (Coral Harbour) 2003 (northern Foxe Basin), and 2005 (Repulse Bay) (Higdon, 2008). From 1918-1988 Inuit from Greenland and Canada killed an estimated 36 bowhead for harvest and another 14 were struck and lost (Higdon, 2008) since the end of commercial whaling, meaning of the 50 whales killed only 72% were harvested. In addition to the 6 recorded kills, a struck and loss rate of 25% was assumed (Ferguson pers. comm.) from 1994 onwards, meaning roughly 1 whale was killed every four years in addition to the 6 recorded kills. The catch for 1970 was set to 1 whale, with no catches until 1994 in the model.

Walrus Hunting

Hunting of walrus has been estimated at 35+ animals for south HB walrus and 230 for NHB walrus each year (NAMMCO, 2005a). However, reported landings are less than half of these estimated values. Hunting for southern walrus occurs in Sanikiluaq, Kuujjuarapik, Umiujaq, and Inukjuak while the Northern Walrus group incurs hunting pressure from Whale Cove, Rankin Inlet, Chesterfield Inlet, Repulse Bay, and Coral Harbour. Catches from 1972-1987 (Strong, 1989) and 1993-2003 from multiple sources summarized in (Stewart and Lockhart, 2005) were used to fit the model. Discrepancies between the two data sets stem from coverage of different communities.

Southern Walrus

For the southern walrus population, the 1972-1987 dataset only includes Sanikiluaq, were the 1993-2003 dataset also includes the communities Ku- ujjuarapik, Umiujaq, and Inukjuak, which almost certainly had catches for the earlier time period. The inclusion of more communities from 1993-2003 may artificially inflate hunting pressure within the model. However, despite the lack of more inclusive data from 1971-1987, the data is used ”as is” and is used as relative catches to fit the model. Catches from 2004 onwards were set to the 2003 reported landings. The 1970 catch was set to 8 animals, the same as the catch in 1972, as there were no records of catches for 1970.

328 A.2. Fisheries Input

Northern Walrus

For the northern walrus population, the same 1971-1987 dataset includes catches from Whale Cove, Rankin Inlet, Chesterﬁeld Inlet, Repulse Bay, and Coral Harbour. The latter dataset also includes Arviat, Ivujivik, Akulivik, and Puvirnituq. Again the catches were used as relative catches to ﬁt the model. Catches from 2004 onwards were set to the reported value to 2003. Starting value for 1970 was 74 walrus, the same as the 1972 landings.

Beluga Hunting

All populations of beluga are hunted, however catch statistics do not distinguish between the stocks. Catches from western HB communities (Baker Lake, Chesterﬁeld Inlet, Coral Harbour, Rankin Inlet, Repulse Bay, Sanikiluaq, and Whale Cove) were presumed to harvest the WHB beluga stock due to proximity. EHB and JB belugas are landed from communities on the eastern side of HB; Kuujjuarapik, Umiujaq, Inukjuak, Puvirnituk, Akulivik, and Ivujivik from Nunavik, and Sanikiluaq from Nunavut, as both groups migrate down the eastern coast of HB to their summering locations. Of the whales landed in Sanikiluaq (Belcher Islands), it was assumed that half were from the JB beluga group, and half were from the EHB beluga group, as tagging studies show eastern HB and JB belugas located around the Belcher Islands (de March and Postma, 2003). For the communities along the eastern coast of HB (Nunavik), the catches were thought to be mostly (70% of catches) from the EHB belugas, as the belugas not only using this as a migration route, but also summering in these areas. JB belugas use the same migration path, but move through to the summering location in James bay, making them available to hunters for a shorter period of time. The remaining 30% of catches from the Nunavik communities was determined to be from the JB beluga group. Catches from 2008-2010 were set to the 2007 value, for all groups. Figure A.2 identiﬁes the trends in beluga harvest rates from 1970-2007 by stock, with landings per community taken from the Joint Commission on narwhal and beluga data (JCNB/NAMMCO, 2009). As in the case with narwhals, a struck and loss rate was incorporated.

329 A.2. Fisheries Input

Figure A.2: Catches of beluga whales form 1970-2007 as aggregated by stock

Reports on 3 communities (1 within the model area) indicates mortality nearly 10 times higher than reported catches when struck and loss rates are considered. When considering loss rates from narwhal, this value appears high. However, as the biomass was adjusted to 50% to account for time within the model area, the catches were not. This assumes double the hunting mortality on all beluga stocks than is reported.

Beluga East

Catches for 1970 were set to the 1974 value of 83 whales, based on catches (JCNB/NAMMCO, 2009), and delineation of catches per community.

Beluga West

Catches for 1970 were set to the 1976 value of 152 whales.

Beluga James

Catches for 1970 were set to the 1974 value of 35 whales.

330 A.2. Fisheries Input

Sealing, Bird Hunting, and Fishing

In some cases catches were inferred based on a per capita basis, for many unregulated species. In these instances the increase in human population is used to calculate an increase in catches. Human community population size was used to estimate the harvest of birds, seals and fish. The human population in the Nunavut portion of Hudson Bay has more than doubled from 1981-2006, increasing from 4686 to 9491 inhabitants (Statistics Canada, 2006), however estimates before this are not available. Using the data from 1981 to 2006, a linear regression was fit to the data to estimate the growth rate giving an R2 value= 0.996 (figure A.3). The growth pattern was assumed to decline constant from the 1970-1981 time period, lacking better data. As community data for Nunavik was not as readily available, population growth was presumed to follow the same growth pattern as communities in Nunavut. In 2006 the total human population for all communities in HB (Nunavut and Nunavik) was 30,117 (Bell, 2002; Statis- tics Canada, 2006; Nunavut Bureau of Statistics, 2008; Sutherland et al., 2010). Following a linear decline in growth rate (figure A.3), this estimated the population to be 10,033 individuals for all Hudson Bay communities in 1970. This value was used to calculate hunting rates for seals, birds, and fish.

Sealing

Seal hunting is not currently regulated, although some estimates have been collected by community for 1975-1985 as summarized in table 14-9 various sources (Stewart and Lockhart, 2005). Based on the number of seals caught, species, and community population, a per capita hunting rate of 1.1 seals·person· year−1 was used. Catches were broken down based on the number of each seal species killed, resulting in 92.6% ringed seals, 6.1% bearded seals, 1% harp seals, and 0.3% harbor seals. The total number of seals caught in 1970 was set to 9110. Number of people was used to drive eﬀort of seal catches, with the proportion of each seal species remaining constant.

331 A.2. Fisheries Input

Figure A.3: Regression of community population size in Nunavut (all communities) from 1981-2006. Line represents model regression over data points.

Bird Hunting

Hunting of birds is not regulated and Inuit do not require a license. Birds, eggs, down and other inedible products can be harvested any time of the year by Cree or Inuit (Migratory Birds Convention Act, 1994). Based on survey records of bird harvests per community during 1975-1985 from table 14-10 (Stewart and Lockhart, 2005), it was estimated that an average of 21.3 birds were harvested for every member of the community. The catches for 1970 were set to 213,703 birds.

Fishing

Fishing rates were based on a per capita rate of 120kg·person·year−1. See Fishing mortality (table A.2 for breakdown of catches. Catches for 1970 were set to 1204 tonnes, with eﬀort being driven by the number of people in the community.

332 A.3. Model Fitting Parameters and Data Sets

A.3 Model Fitting Parameters and Data Sets

Time series data (table A.6) was read in as catches or abundance trends. For unregulated fisheries or hunting activities based on the size of the human population (fishing, bird hunting, and sealing), effort was driven by human population size (figure A.3).

Table A.6: Name and type of time series data used to ﬁt the Hudson Bay Ecosim model

Data set Type of Time series data Bowhead Abundance Relative Abundance Bowhead Catches Forced Catches Foxe Basin Polar Bear Abundance Relative Abundance Foxe Basin Polar Bear Catches Relative Catches Western HB Polar Bear Abundance Relative Abundance Western HB Polar Bear Catches Forced Catches Southern HB Polar Bear Catches Relative Abundance Narwhal Catches Forced Catches Eastern HB Beluga Abundance Relative Abundance Eastern HB Beluga Catches Forced Catches Western HB Beluga Abundance Relative Abundance Western HB Beluga Catches Forced Catches James Bay Beluga Abundance Relative Abundance James Bay Beluga Catches Forced Catches Northern HB Walrus Catches Forced Catches Southern HB Walrus Catches Forced Catches Killer Whale Abundance Forced Abundance Killer Whale Catches Forced Catches Arctic Cod Abundance Relative Abundance Sculpin/Zoarcid Abundance Relative Abundance Capelin Abundance Relative Abundance Sandlance Abundance Relative Abundance

Forcing Functions

The model is based on an understanding of the eﬀects of climate change on the ecosystem. Warmer air temperatures, caused by climate change, have altered the mean ice freeze-up and break-up dates by 0.8-1.6 weeks in spring and fall (Hochheim et al., 2010). Figure 2.2 uses data from the HadISST (Hadley Centre Sea Ice and Sea Surface Temperature data set) model (BADC, 2010) to show the average % cover of sea ice for HB by month, with 95% CI. Starting in June, the variation in average ice cover increases, with June, July, November, and December having the greatest

333 A.3. Model Fitting Parameters and Data Sets variance in ice cover. The SST also becomes increasingly variable from June to December, and it is these changes in temperature and ice freeze-up and break-up dates that are thought to be important driver in the ecosystem and hence are implemented in the model. The availability of ice algae within the model is contingent upon the presence of sea ice, therefore the ice algae group was driven through a forcing function (FF) in the model. The sea ice FF was applied to the ice algae group, as a multiplier of the production rate using the average % cover of sea ice of all cells in the model area. The data was re-scaled to all positive values with a mean value of 1 for the ﬁrst year (1970). The pelagic production functional group was also driven in the model through SST, using the same HadISST dataset. Figure 2.2 shows the annual SST average for HB by month, with 95% CI. Again, data was re-scaled to positive values with a mean value of 1 for 1970.

Mediation Functions

Figure A.4: Polar Bear Mediating Function with ice algae as the mediating group (x−axis). Y−axis shows the relative weight of polar bears, starting at y= 1 (Ecopath value).

In order to ﬁt the polar bear groups (FB, WHB, SH), a mediation function was used. Sea ice is critical to polar bear foraging, as they use the ice as a hunting surface (Stirling and Derocher, 1993). Declines in the western HB polar bear population from 1981-1998 have been linked to earlier breakup of the ice in the spring, and has been shown to cause reproductive stress

334 A.3. Model Fitting Parameters and Data Sets and decreased body condition (Stirling et al., 1999). These effects have only been shown to be significant for the western HB population, as the timing of sea ice break-up has changed only on the west coast of HB (Stirling et al., 1999; Stirling and Parkinson, 2006). A mediation function was applied to all polar bear groups, based on the changes in western HB. A sigmoid shape function was used with ice algae as the mediating group. As the biomass of ice algae increases (which is driven by the % sea ice cover, making it a proxy for sea ice), polar bears have a larger foraging area and their prey becomes more vulnerable to them. For the starting point, near the top of the curve was selected, as changes in the sea ice have been documented locally since the 1980s (Gaston et al., 2009b). The sigmoid shape was selected, as it is believed once the sea ice reaches a maximum/minimum, there is no added benefit/detriment to polar bears. The reference point on the curve which crosses the y-axis at 1 indicates the 1970 or starting value of the model, meaning increases in sea ice will have smaller effects on polar bears than decreases in sea ice. Although declines in the Foxe Basin and southern HB populations of polar bears are not believed to be as extreme as WHB, it is highly likely they will respond to declines in sea ice the same way. Therefore the same mediation function was applied to all polar bear functional groups.

Biomass Accumulation

Abundance of Eastern HB belugas has declined from 1985-2008 (Hammill, 2001; Gosselin, 2005; Hammill et al., 2009), however the model was unable to capture this decreasing trend through hunting and predation alone. More- over, the model was unable to capture the large increases in the JB beluga population. As the JB stock of belugas is genetically different from EHB belugas, it is hypothesized this stock is a constant mixture of other stocks (de March and Postma, 2003). Migration from EHB belugas to JB belugas was incorporated into the model in the form of biomass accumulation to assist in fitting. A decrease of 0.5% y−1 was necessary to fit the observed declines of EHB belugas. This led to an increased biomass accumulation of

335 A.4. Model Parameterization and Output

1% y−1 to JB belugas, as the biomass of this group was roughly half the EHB biomass. Both P/B values were adjusted to accommodate for these changes; EHB beluga P/B was decreased from 0.0758 to 0.0658 y−1, and JB beluga P/B was increased from 0.0673 y−1 to 0.0873 y−1. A positive biomass accumulation rate was also used for bowhead whales, as the population is still rebounding from heavy commercial harvests (Higdon 2008 unpublished data), and the increases were not able to be captured by the model. A rate of 2% y−1 was initially used, however this value was later lowered to 0.7% y−1, and was still able to capture the increase.

Group Info Parameters

The default maximum relative feeding time default of 2 was used for all species except marine mammals where it was set to 10 for all whale species (killer, narwhal, bowhead, and belugas), and 5 for all pinniped groups (walrus, harp, ringed, bearded, and harbor seals). The feeding time adjustment rate default of 0 was used for all species groups except marine mammals where it was set to 0.5 (Christensen et al., 2005, 2007).

Vulnerabilities

Vulnerabilities were first estimated using the automated fit to time series routine in Ecosim (Buszowski et al., 2009). Next, the vulnerabilities for individual predator prey interactions were adjusted to fit the model more accurately to time series data. All vulnerabilities are displayed in appendix E.

A.4 Model Parameterization and Output

Model Balancing

Many parameters were reﬁned during the balancing process, through a series of steps. A general outline of the progression is presented, although adjustments to the diets were also made but not noted. Final parameter values

336 A.4. Model Parameterization and Output of the balanced model are presented in table A.7. The general processes of balancing the model follows.

• After creating all the functional groups and calculating general parameters, and diets, fishing groups were created. Once the catches for 1970 were determined, P/B ratios were adjusted to include hunting and fishing mortalities. After adjusting the P/B for marine mammals, birds, and fish, the P/B of fish had to be increased further.

• The equation used to calculate P/B for ﬁsh often underestimates higher latitude species (Pauly, 1980), and the smaller P/B was causing the model to estimate large biomasses of ﬁsh. Consequently, these ratios were increased to the upper limits based on the species found within the functional group.

• Many of the zooplankton groups lacked region speciﬁc data for P/B and Q/B, therefore a P/Q ratio of 0.25 was assumed, so the model could estimate an additional parameter.

• The EE of birds was too high indicating too much mortality. The P/B ratio was increased to allow enough hunting and predation mortality to occur in the model. Impacts of each functional group upon others are presented in appendix F, as output from the mixed trophic impact table in Ecopath.

Monte Carlo Simulations

Monte Carlo simulations were run using the pedigree ranking from Ecopath version 5 (Christensen et al., 2005). C.V. values were estimated based on quality of input data (see appendix G for all CV values and appendix H for graphs of biomass and P/B results). MC simulations were unable to improve the sum of squares value obtained by ﬁtting the model. However, ranges of plausible ranges were obtained for biomass and P/B parameters. Biomass input CV and output with limits are presented in table A.8.

337 A.4. Model Parameterization and Output

Table A.7: Balanced Ecopath model with parameters estimated by the model in bold. Biomass is presented in (t · Km−2). Production/Biomass (P/B) and Consumption/Biomass (Q/B) are presented as an annual rate (y−1). Trophic Level (TL), Ecotrophic Eﬃciency (EE) and Produc- tion/Consumption (P/Q) values are dimensionless.

Group name TL B P/B Q/B EE P/Q WHB Polar Bear 4.857 0.0005 0.129 2.08 0.414 0.062 SH Polar Bear 4.906 0.0004 0.154 2.08 0.506 0.074 Polar Bear Foxe 4.927 0.0002 0.121 2.08 0.304 0.058 Killer Whale 4.872 0 0.151 4.998 0.265 0.03 Narwhal 4.062 0.0019 0.084 26.182 0.271 0.003 Bowhead 3.335 0.0109 0.021 5.475 0.384 0.004 Walrus N 3.332 0.0027 0.172 47.123 0.188 0.004 Walrus S 3.452 0.001 0.097 33.778 0.143 0.003 Bearded Seal 3.866 0.0037 0.176 14.262 0.791 0.012 Harbour Seal 3.971 0.001 0.125 18.612 0.074 0.007 Ringed Seal 4.077 0.0469 0.158 17.272 0.413 0.009 Harp seal 4.103 0.001 0.126 15.66 0.688 0.008 Beluga E 3.694 0.0021 0.066 21.448 0.22 0.003 Beluga W 3.873 0.0247 0.064 16.713 0.133 0.004 Beluga James 3.869 0.0015 0.087 16.623 0.679 0.005 Seabirds 3.839 0.065 0.37 17.258 0.95 0.021 Arctic Char 3.3 0.412 0.2 1.5 0.95 0.133 Atlantic Salmon 3.45 0.148 0.52 7.15 0.95 0.073 Gadiformes 3.235 0.853 0.47 1.85 0.95 0.254 Sculpins/ Zoarcids 3.188 0.382 0.7 3.269 0.95 0.214 Capelin 3.132 0.488 1.7 4.8 0.95 0.354 Sandlance 3.128 0.705 0.85 3.45 0.95 0.246 Sharks/Rays 4.033 3.18E-06 0.22 1.25 0.95 0.176 Other Marine Fish 2.948 0.374 0.932 3.018 0.95 0.309 Brackish Fish 3.216 0.055 3.5 5.798 0.95 0.604 Cephalopods 3.645 0.227 1.5 5 0.95 0.3 MacroZooplankton 2.711 7.5 1 3 0.278 0.333 Euphausiids 2.787 2.148 3.3 13.2 0.8 0.25 Copepods 2.05 4.015 16 64 0.472 0.25 Crustaceans 2.41 1.8 3.6 14.4 0.584 0.25 Other MesoZooplankton 2.336 1.21 10 40 0.556 0.25 MicroZooplankton 2 2.235 15 45 0.95 0.333 Marine Worms 2.275 5.93 0.6 4 0.95 0.15 Echinoderms 2.575 8.708 0.3 1 0.95 0.3 Bivalves 2.148 5.942 0.57 6.3 0.95 0.091 Other Benthos 2.091 3.139 2.5 12.5 0.95 0.2 Pelagic Production 1 8 46.865 - 0.8 - Ice Algae 1 3.5 46.197 - 0.65 - Ice Detritus 1 0.009 - - 0.904 - Detritus 1 0.33 - - 0.224 -

338 A.4. Model Parameterization and Output

Table A.8: CV used for Monte Carlo estimates of biomass. Results show the mean biomass (B), along with the upper and lower limits of the 95% CI presented in t·km−2

Functional Group B (CV) Lower Limit Mean B Upper Limit 1 Polar Bear WHB 0.15 0 0 0.001 2 SH Polar Bear 0.15 0 0 0 3 Polar Bear Foxe 0.15 0 0 0 4 Killer Whale 0.15 0 0 0 5 Narwhal 0.15 0.001 0.002 0.003 6 Bowhead 0.4 0.002 0.011 0.02 7 Walrus N 0.25 0.001 0.003 0.004 8 Walrus S 0.25 0 0.001 0.001 9 Bearded Seal 0.25 0.002 0.004 0.006 10 Harbour Seal 0.25 0.001 0.001 0.002 11 Ringed Seal 0.25 0.023 0.047 0.07 12 Harp seal 0.25 0.001 0.001 0.002 13 Beluga E 0.15 0.001 0.002 0.003 14 Beluga W 0.15 0.017 0.025 0.032 15 Beluga James 0.15 0.001 0.001 0.002 16 Seabirds 0.4 0.013 0.065 0.117 17 Arctic Char 0.1 0.329 0.412 0.494 18 Atlantic Salmon 0.1 0.118 0.148 0.177 19 Gadiformes 0.1 0.683 0.853 1.024 20 Sculpins/ Zoarcids 0.1 0.305 0.382 0.458 21 Capelin 0.1 0.39 0.488 0.585 22 Sandlance 0.1 0.564 0.705 0.846 23 Sharks/Rays 0.1 0 0 0 24 Other Marine Fish 0.1 0.3 0.374 0.449 25 Brackish Fish 0.1 0.044 0.055 0.066 26 Cephalopods 0.25 0.113 0.227 0.34 27 Macro-Zooplankton 0.25 3.75 7.5 11.25 28 Euphausiids 0.15 1.504 2.148 2.792 29 Copepods 0.15 2.811 4.015 5.22 30 Crustaceans 0.15 1.26 1.8 2.34 31 Other Meso-Zoopl. 0.15 0.847 1.21 1.573 32 Micro-Zooplankton 0.25 1.117 2.235 3.352 33 Marine Worms 0.1 4.744 5.93 7.115 34 Echinoderms 0.1 6.966 8.708 10.449 35 Bivalves 0.1 4.753 5.942 7.13 36 Other Benthos 0.1 2.511 3.139 3.767 37 Primary Production 0.15 5.6 8 10.4 38 Ice Algae 0.15 2.45 3.5 4.55

339 A.4. Model Parameterization and Output

Most marine mammal biomass results remained quite close to the starting value. Ringed seals had the largest starting biomass of any marine mammal group, and also the highest upper limit or largest biomass which could be supported by the system, followed by WHB Bay beluga and bowhead whales. Ringed seals had a large uncertainty, as population sizes are not well known, however the model is able to support a large biomass of these seals. Within the model framework, bowheads have the potential to double the biomass and still be supported by the ecosystem. Although there was high uncertainty with the biomass of fish groups, the ability of the system to sustain moderate biomasses of fish is an added discovery due to the understudied nature of fish within the ecosystem. While commercial fishing endeavors have not been profitable, it would be assumed the region has a conservative fish biomass. Compared to other ecosystem models, total fish biomass is lower than other systems of similar latitude. To- tal fish biomass of HB is 3.42t·km−2 compared to 4.32t·km−2 in the Antarc- tic Peninsula (Chapter 3), although the Antarctic is more productive, the dominant species is krill (Euphausia superba), and commercial fisheries operations in this region have also proved difficult. Total zooplankton biomass of 18.91t·km−2 appears to fall within the ranges of observed samples. Harvey et al. (2006), estimated macro and meso-zooplankton from 10-20t·km−2 for central HB, while a few samples from Harvey et al. (2001) reached close to 50t·km−2 in northern HB17 . However these high values were obtained from late summer values, and are likely not representative of an annual value.

Ecosim Fitting

Results of time series fitting, including effort and mediation are presented in figure 2.5. While most trends were captured by the model, there were a few exceptions. Foxe Basin polar Bear catch was not forced due to the unknown portion of catches coming from within the model area. Therefore it was presented as a relative catch sequence. Although the values for the

17Biomass was 5.5g · m−2 dry weight using a conversion of 9g WW=1g DW (Pauly and Christensen, 1995)

340 A.4. Model Parameterization and Output data and the model are not the same, the trend appears to be similar, with catches decreasing and leveling out by the late 1980s. James Bay beluga abundance was not able to increase to levels as high as survey estimates. While migration from the EHB beluga group (through biomass accumulation) improved the fit for both EHB and James Bay belugas, the full magnitude of the increase was unable to be fully captured within the model. Data for fitting fish groups provided insight as to general trends of abundance, however the model was unable to simulate the extreme increase in capelin and sandlance populations, as well as the full decreases in gadiformes and sculpins/zoarcids. Biomass accumulation was crucial to obtaining fits for bowhead and EHB belugas. Bowheads were unable to increase as rapidly within the model, starting at such a low biomass, and a low P/B. Conversely, a small decline in EHB belugas was created through hunting mortality and vulnerability settings, but was not fully captured until a negative biomass accumulation component was added. All polar bear groups demonstrated stable population sizes with hunting pressure. Vulnerabilities were able to cause small increases or decreases in the populations, however, the addition of mediation increased the sensitivity of these groups to changes in sea ice as well as vulnerabilities of their prey. Once the mediation function was applied (to arena area and vulnerability of prey), all polar bear groups became highly sensitive to small changes in vulnerabilities.

Ecosim Output

Starting from the bottom of the food web, shifts caused by forcing functions can be identiﬁed. Figure A.5 identiﬁes changes in the lowest trophic levels of the ecosystem, with declines in ice algae and ice detritus of nearly 10% each, and increases in pelagic production (26%), and pelagic detritus (33%). Since both the ice algae and the pelagic production groups were forced, these changes are not surprising. Changes in the detritus and producers are propagated to the next trophic

341 A.4. Model Parameterization and Output

Figure A.5: Model end biomass for 2010 presented as percentage change from starting biomass for producers and detritus. levels, as shown in figure A.6 by declines in all benthic groups, with the exception of crustaceans (although this group contains pelagic and benthic crustaceans). Zooplankton, however, fare much better, with increases ranging from 12% (micro-zooplankton) to 58% (macro-zooplankton). The increase in zooplankton is caused by the diets containing large concentrations of pelagic production, which supersede the declines in the ice algae contribution of the diet. Declines are identified predominantly in benthic fish (Gadiformes: Arctic and Polar cod, Sculpins/Zoarcids: benthic fish, and sharks/rays) due to diets consisting of ice detritus and other benthos (figure A.7). Gadiformes and sculpins/zoarcids decreased in the diet of thick-billed murres an average of 68 and 57%, respectively (Gaston et al., 2003)18. Pelagic based fish show increases, with the largest being capelin and sandlance. Fitting of time- series data (figure 2.5) from the diet of thick-billed murres, appears to be unable to capture the full magnitude of the increase for both capelin and sandlance. Capelin increased in the diet from 20 to 50%, and sandlance from 4 to 20% (as averaged from the first and last 3 years). In the model

18When comparing the average contribution to the diet of thick billed murres as averaged over the ﬁrst three and last three years of the diet study

342 A.4. Model Parameterization and Output

Figure A.6: Model ending biomass for 2010 presented as percentage change from the starting biomass for zooplankton and benthic groups these groups show substantial increases with capelin increasing over 70% of their original biomass, with sandlance nearly doubling. Increased hunting and fishing pressure for birds and fish groups does not appear to be causing declines, as the mortality caused by hunting and fishing for these groups was quite small in relation to total mortality (table A.2). Seabird biomass was still able to increase within the model, despite hunting effort increasing roughly 4.5 times the 1970 effort. Most marine mammal functional groups were fit to abundance data, therefore changes in biomass were previously known. All polar bear groups declined in biomass (figure A.8), primarily due to the mediation function hindering their ability to hunt effectively when there is less sea ice. Narwhal decreases are due to increasing hunting mortality. Biomass for narwhal remains relatively stable from 1970-2000. However, when catches are increased from 2000-2010, the population begins to decline, but only in the last 10 years of the simulation, indicating this is the result of hunting pressure (see narwhal graph in appendix I). Removal of catches in the model identifies an increase in narwhal biomass. Bearded seals also appear to decline due to hunting mortality (figure A.8). For bearded seals, hunting

343 A.4. Model Parameterization and Output

Figure A.7: Model ending biomass for 2010 presented as percentage change from starting biomass for fish and seabirds. mortality accounts for one third of all mortality in Ecopath. Combined with the increases in human population and hunting pressure, by 2010 the hunting mortality is nearly 10 times the predation mortality indicating harvest of bearded seals is causing the decline within the model. The harp seal group also shows hunting mortality to increase to double the predation mortality by the end of the simulation. However, because catches for this group were set low in the first year, large increases in catch are still unable to cause a decline overall. Ringed and harbor seals show low hunting mortality throughout the simulation, indicating the populations are large enough to sustain the effort levels used in the model fitting. Both walrus groups (N and S) experience less predation from polar bears, due to declining populations. N walrus increase in the model due to low harvest levels from 2003-2010. S walrus experienced higher hunting pressure during this time, causing the decrease observed at the end of the model simulation. Killer whale abundance was forced within the model to replicate the observed increase in killer whales. Biomass accumulation was unable to

344 A.4. Model Parameterization and Output

Figure A.8: Model ending biomass for 2010 presented as percentage change from starting biomass for marine mammal groups. explain this large increase, leading the authors to believe the changes may be caused by immigration from other areas.

345 Appendix B

Marine Mammal Mortality Equations

Mortality for marine mammal functional groups was calculated based on life history information and estimates of longevity (L(x)), using equation B.1 to estimate the probability of survivorship from birth to age x, with information from equations B.2 to B.4, and parameters in table B.1.

L(x) = Lj(x) · Lc(x) · Ls(x) (B.1)

Lj(x) = exp[(−a1/b1) · 1 − exp(−b · x/Ω)] (B.2)

Lc(x) = exp[−a2 · x/Ω] (B.3)

Ls(x) = exp[a3/b3) · 1 − exp(b3 · x/Ω)] (B.4)

Where Lj(x) is the mortality due to juvenile factors, Lc(x) is the constant mortality experienced by all age classes, and Ls(x) is the mortality sue to senescent factors. Constant parameters a1, a2, a3, b1, andb3 allow ﬂex- ibility in the shape of the survivor curve depending on life history traits of the species. For all pinniped groups survivorship curve parameters from northern fur seals were used to estimate survivorship (table B.1). Human survivorship parameter were used for killer whales and sperm whales, as there are few to zero predators on these groups, likely causing lowered juvenile mortality. Baleen whale (ﬁn, minke, blue, humpback) survivorship was calculated using monkey and human survivorship parameters, however the monkey parameters were used as they had a slightly higher juvenile mortality. This was believed to be more representative of baleen whale sur-

346 Appendix B. Marine Mammal Mortality Equations

Table B.1: Marine mammal survivorship curve parameters based on life histories of fur seals, monkeys, and humans.

Species group a1 a2 a3 b1 b3 Northern Fur Seal 14.343 0.1710 0.0121 10.259 6.6878 Old World Monkeys 30.430 0.0000 0.7276 206.720 2.3188 Human (female) 40.409 0.4772 0.0047 310.360 8.0290 vivorship. Mortality was calculated as 1- the survivorship for each year of longevity, and averaged over all ages (x) to give the P/B value.

347 Appendix C

Hudson Bay Bird Species

Table C.1: Bird species found within the Hudson Bay model area by family, as reported from Stewart and Lockhart (2005).∗Indicates the species is rare in its distribution within the model area.

Common Name Species Name Family Gaviidae: Loons red-throated loon Gavia stellata (Pontoppidan, 1763) Paciﬁc loon G. paciﬁca (Lawrence) common loon G. immer (Brnnich) yellow-billed loon G. adamsii (Gray)∗

Family Podicipedidae: Grebes pied-billed grebe Podilymbus podiceps (Linnaeus)∗ horned grebe Podiceps auritus (Linnaeus)

Family Procellariidae: Fulmars northern fulmar Fulmarus glacialis (Linnaeus)∗

Family Hydrobatidae: Storm-petrels Leach’s storm-petrel Oceanodroma leucorhoa (Viellot)∗

Family Pelecanidae: Pelicans American white pelican Pelecanus erythrorhynchos Gmelin∗

Family Sulidae: Gannets northern gannet Sula bassanus (Linnaeus)∗

Family Phalacrocoracidae: Cormorants double crested cormorant Phalacrocorax auritus (Lesson)

Family Ardeidae: Herons and Bitterns American bittern Botaurus lentiginosus (Rackett) great blue heron Ardea herodias Linnaeus snowy egret Egretta thula (Molina)∗ little blue heron E. caerulea (Linnaeus)∗ Continued on Next Page

348 Appendix C. Hudson Bay Bird Species

Table C.1 Continued Common Name Species Name tricolor heron E. tricolor (Mller)∗ black-crowned night heron Nycticorax nycticorax (Linnaeus)∗

Family Anatidae: Geese, Swans, and Ducks greater white-fronted goose Anser albifrons (Scopoli) snow goose Chen caerulescens (Linnaeus) Ross’s goose C. rossii (Cassin) Canada goose Branta canadensis (Linnaeus) Brant B. bernicla (Linnaeus) trumpeter swan Cygnus buccinator Richardson∗ tundra swan C. columbianus (Ord) gadwall Anas strepera Linnaeus∗ Eurasian widgeon A. penelope Linnaeus∗ American widgeon (baldpate) A. americana Gmelin American black duck A. rubripes Brewster mallard A. platyrhynchos Linnaeus blue winged teal A. discors Linnaeus northern shoveler A. souchet Linnaeus northern pintail A. acuta Linnaeus green-winged teal A. crecca Linnaeus canvasback Aythya valisineria (Wilson)∗ redhead A. americana (Eyton)∗ ring-necked duck A. collaris (Donovan) greater scaup A. marila (Linnaeus) lesser scaup A. aﬃnis (Eyton) king eider Somateria spectabilis (Linnaeus) common eider S. mollissima (Linnaeus) harlequin ducks Histrionicus histrionicus (Linnaeus) surf scoter Melanitta perspicillata (Linnaeus) white-winged scoter M. fusca (Linnaeus) black scoter (common scoter) M. nigra (Linnaeus) long-tailed duck (oldsquaw) Clangula hyemalis (Linnaeus) buﬄehead Bucephala albeola (Linnaeus)∗ common goldeneye B. clangula (Linnaeus) Barrow’s goldeneye B. islandica (Gmelin)∗ hooded merganser Lophodytes cucullatus (Linnaeus)∗ common merganser Mergus merganser Linnaeus red-breasted merganser M. serrator Linnaeus ruddy duck Oxyura jamaicensis (Gmelin)∗

Family Accipiteridae: Ospreys, Eagles, Hawks, and Allies osprey Pandion haliaetus (Linnaeus) bald eagle Haliaeetus leucocephalus (Linnaeus) northern harrier (marsh hawk) Circus cyaneus (Linnaeus) Continued on Next Page

349 Appendix C. Hudson Bay Bird Species

Table C.1 Continued Common Name Species Name northern goshawk Accipter gentilis (Wilson)∗ sharp-shinned hawk A. striatus Vieillot rough-legged hawk Buteo lapopus (Gmelin) golden eagle Aquila chrysaetos (Linnaeus)∗

Family Falconidae: Falcons merlin Falco columbarius Linnaeus peregrine falcon F. peregrinus Tunstall gyrfalcon F. rusticolus Linnaeus prairie falcon F. mexicanus Schlegel∗

Family Rallidae: Rails, Gallinules, and Coots yellow rail Coturnicops noveboracensis (Gmelin) sora Porzana carolina (Linnaeus) American coot Fulica americana Gmelin

Family Gruidae: Cranes sandhill crane Grus canadensis (Linnaeus)

Family Charadriidae: Plovers black-bellied plover Pluvialis squatarola (Linnaeus) American golden-plover P. dominica (Muller) semipalmated plover Charadrius semipalmatus Bonaparte killdeer C. vociferus Linnaeus

Family Scolopacidae: Sandpipers, Phalaropes, and allies greater yellowlegs Tringa melanoleuca (Gmelin) lesser yellowlegs T. ﬂavipes (Gmelin) solitary sandpiper T. solitaire Wilson spotted sandpiper Actitis macularia (Linnaeus) whimbrel Numenius phaeopus (Linnaeus) Hudsonian godwit Limosa haemastica (Linnaeus) marbled godwit L. fedoa (Linnaeus) ruddy turnstone Arenaria interpres (Linnaeus) red knot Calidris canutus (Linnaeus) sanderling C. alba (Pallas) semipalmated sandpiper C. pusilla (Linnaeus) little stint C. minuta (Leisler)∗ least sandpiper C. minutilla (Vieillot) white-rumped sandpiper C. fuscicollis (Vieillot) Baird’s sandpiper C. bairdii (Coues) pectoral sandpiper C. melanotos (Vieillot) purple sandpiper C. maritima (Brunnich) dunlin C. alpina (Linnaeus) Continued on Next Page

350 Appendix C. Hudson Bay Bird Species

Table C.1 Continued Common Name Species Name stilt sandpiper C. himantopus (Bonaparte) buﬀ-breasted sandpiper Tryngites subruﬁcollis (Vieillot) short-billed dowitcher Limnodromus griseus (Gmelin) Wilson’s snipe Gallinago delicata Ord Wilson’s phalarope Phalaropus tricolor (Vieillot) red-necked/northern phalarope P. lobatus (Linnaeus) red phalarope P. fulicaria (Linnaeus)

Family Laridae: Jaegers, Gulls, and Terns Pomeranian jaeger Stercorarius pomarinus (Temminick) parasitic jaeger S. parasiticus (Linnaeus) long-tailed jaeger S. longicaudus Vieillot laughing gull Larus atricilla Linnaeus∗ Franklin’s gull L. pixican Wagler∗ little gull Larus minutus Pallas black-headed gull L. ridibundus Linnaeus∗ Bonaparte’s gull L. philadelphia (Ord) mew gull L. canus Linnaeus∗ ring-billed gull L. delawarensis Ord California gull L. californicus Lawrence∗ herring gull L. argentatus Pontoppidan Iceland gull L. glaucoides Meyer lesser black-backed gull L. fuscus Linnaeus∗ glaucous -winged gull L. glaucescens Naumann∗ glaucous gull L. hyperboreus Gunnerus great black-backed gull L. marinus Linnaeus∗ black-legged kittiwake Rissa tridactyle (Linnaeus) Ross’s gull11 Rodostethia rosea (MacGillivray) Sabine’s gull Xema sabini (Sabine) ivory gull12 Pagophila eburnea (Phipps)∗ Caspian tern Sterna caspia Pallas common tern S. hirundo Linnaeus Arctic tern S. parasisaea Pontoppidan Forster’s tern S. forsteri Nuttall∗ white-winged tern Chlidonias leucopterus (Temminck)∗ black tern C. niger (Linnaeus)

Family Alcidae: Auks, Murres, and Puﬃns Dovekie Alle alle (Linnaeus) thick-billed murre Uria lomvia (Linnaeus) black guillemot Cepphus grylle (Linnaeus)

Family Strigidae: Typical owls snowy owl Nyctea scandiaca (Linnaeus) Continued on Next Page

351 Appendix C. Hudson Bay Bird Species

Table C.1 Continued Common Name Species Name short-eared owl Asio ﬂa meus (Pontoppidan)

Family Alcedinidae: Kingﬁshers belted kingﬁsher Ceryle alcyon (Linnaeus)

Family Corvidae: Crows and Ravens American crow Corvus brachyrhynchos Brehm common raven C. corax Linnaeus

Family Alaudidae: Larks horned lark Eremophila alpestris

Family Motacillidae: Pipits American pipit Anthus rubescens (Tunstall)

352 Appendix D

Hudson Bay Fish Species

Table D.1: Fish functional groups and species included in each group.

Common Name Species Name Arctic Char: Arctic Char Salvelinus alpinus

Atlantic Salmon: Atlantic Salmon Salmo salar

Gadiformes: polar cod Arctogadus glacialis Arctic cod Boreogadus saida Greenland cod Gadus ogac

Sculpins/ Zoarcids: Arctic staghorn Gymnocanthus tricuspis twohorn sculpin Icelus bicornis spatulate sculpin Icelus spatula fourhorn sculpin Myoxocephalus quadricornis Arctic sculpin Myoxocephalus scorpioides shorthorn sculpin Myoxocephalus scorpius moustache sculpin Triglops murrayi ribbed sculpin Triglops pingelli ﬁsh doctor Gymnelus viridis pale eelpout Lycodes pallidus Arctic eelpout Lycodes reticulatus

Brackish Fish: Arctic shanny Stichaeus punctatus slender eelblenny Lumpenus fabricii righteye ﬂounder Pleuronectidae sp. Canadian plaice Hippoglossoides platessoides

Capelin: Capelin Mallotus villosus Continued on Next Page

353 Appendix D. Hudson Bay Fish Species

Table D.1 Continued Common Name Species Name

Sandlance: northern sand lance Ammodytes dubius stout sand lance Ammodytes hexapterus

Sharks/Rays: sleeper sharks Somniosidae skates Rajidae

Other Marine Fish: alligator poacher Leptagonus decagonus Atlantic alligatorfish Ulcina olriki lumpfish Cyclopterus lumpus leatherfin lumpsucker Eumicrotremus derjugini Atlantic spiny lumpsucker Eumicrotremus spinosus sea tadpole Careproctus reinhardti gelatinous snailfish Liparis fabricii dusky snailfish Liparis gibbus kelp snailfish Liparis tunicatus stout eelblenny Anisarchus medius fourline snakeblenny Eumesogrammus praecisus daubed shanny Leptoclinus maculatus banded gunnel Pholis fasciata Atlantic Herring Clupea harengus

354 Appendix E

Hudson Bay Model Vulnerabilities

Table E.1: Vulnerabilities used in the ﬁtting of the Hudson Bay model

Prey/predator 1 2 3 4 5 6 7 8 9 10 11 12 1 Polar Bear WHB 2 2 SH Polar Bear 3 3 Polar Bear Foxe 2 4 Killer Whale 5 Narwhal 10 6 Bowhead 10 7 Walrus N 2 10 8 Walrus S 3 10 9 Bearded Seal 2 3 2 10 2 10 Harbour Seal 2 3 2 10 11 Ringed Seal 2 3 2 10 10 12 Harp seal 2 3 2 10 13 Beluga E 3 2 14 Beluga W 2 2 2 15 Beluga James 10 2 16 Seabirds 2 3 2 10 17 Arctic Char 1 1 18 Atlantic Salmon 10 1 1 1 19 Gadiformes 10 1 10 2 2 10 10 2 20 Sculpins/Zoarcids 10 1 2 2 1 10 10 1 21 Capelin 1 10 2 1 1 22 Sandlance 10 2 1 23 Sharks/Rays 2 24 Other Marine Fish 2 10 1 10 10 1 2 2 1 25 Brackish Fish 2 1 1 2 26 Cephalopods 2 10 1 1 2 2 27 MacroZooplankton 2 1 2 1 2 2 28 Euphausids 2 1 2 2 2 29 Copepods 2 2 30 Crustaceans 2 1 2 10 10 1 2 2 1 31 Other MesoZooplankton 2 2 32 MicroZooplankton 2 2 33 Marine Worms 2 2 10 10 10 10 34 Echinoderms 10 10 2 2 10 10 10 10 35 Bivalves 10 10 2 2 10 10 36 Other Benthos 10 2 2 10 10 10 10 10 37 Primary Production 2 2 2 38 Ice Algae 2 39 Ice Detritus 2 40 Pelagic Detritus 2 2 Table Continued on Next Page

355 Appendix E. Hudson Bay Model Vulnerabilities

Table E.1 Continued Prey/Predator 13 14 15 16 17 18 19 20 21 22 23 24 1 Polar Bear WHB 2 SH Polar Bear 3 Polar Bear Foxe 4 Killer Whale 5 Narwhal 2 6 Bowhead 7 Walrus N 8 Walrus S 9 Bearded Seal 2 10 Harbour Seal 11 Ringed Seal 2 12 Harp seal 2 13 Beluga E 14 Beluga W 15 Beluga James 16 Seabirds 2 17 Arctic Char 2 2 1 2 18 Atlantic Salmon 2 2 2 2 1 1 2 19 Gadiformes 1 10 2 2 2 10 2 1 20 Sculpins/ Zoarcids 1 2 2 2 10 2 1 21 Capelin 2 2 1 2 1 1 10 10 10 1 22 Sandlance 2 2 1 1 10 10 10 23 Sharks/Rays 2 24 Other Marine Fish 2 2 1 1 10 10 2 25 Brackish Fish 2 2 2 1 1 2 26 Cephalopods 2 2 1 2 1 1 2 1 27 MacroZooplankton 2 2 1 1 1 1 2 1 28 Euphausids 2 2 2 2 1 1 2 2 2 1 29 Copepods 2 2 2 2 1 1 2 2 1 30 Crustaceans 2 2 2 1 1 2 2 2 2 2 1 31 Other MesoZooplankton 2 1 1 2 2 1 32 MicroZooplankton 1 1 2 2 1 33 Marine Worms 10 10 10 10 10 10 10 10 10 34 Echinoderms 10 10 10 10 10 35 Bivalves 10 10 10 10 10 36 Other Benthos 10 10 10 10 10 10 10 10 10 37 Primary Production 1 1 1 1 1 38 Ice Algae 1 1 2 2 1 39 Ice Detritus 2 2 40 Pelagic Detritus 1 1 1 1 Table Continued on Next Page

356 Appendix E. Hudson Bay Model Vulnerabilities

Table E.1 Continued Prey/Predator 25 26 27 28 29 30 31 32 33 34 35 36 1 Polar Bear WHB 2 SH Polar Bear 3 Polar Bear Foxe 4 Killer Whale 5 Narwhal 6 Bowhead 7 Walrus N 8 Walrus S 9 Bearded Seal 10 Harbour Seal 11 Ringed Seal 12 Harp seal 13 Beluga E 14 Beluga W 15 Beluga James 16 Seabirds 17 Arctic Char 2 18 Atlantic Salmon 2 19 Gadiformes 10 20 Sculpins/Zoarcids 10 21 Capelin 2 1 22 Sandlance 2 1 23 Sharks/Rays 24 Other Marine Fish 2 25 Brackish Fish 1 26 Cephalopods 1 2 27 MacroZooplankton 1 1 1 1 1 28 Euphausids 1 1 2 1 2 1 2 2 29 Copepods 1 1 2 1 1 1 1 2 1 30 Crustaceans 1 1 2 1 1 2 2 2 31 Other MesoZoopl. 1 1 2 1 2 1 1 2 1 1 32 MicroZoopl. 1 1 2 1 2 2 1 1 2 1 1 33 Marine Worms 10 10 10 10 10 34 Echinoderms 10 10 10 10 10 35 Bivalves 10 10 10 36 Other Benthos 10 10 10 10 10 37 Primary Production 1 1 1 1 1 1 1 1 1 1 1 38 Ice Algae 1 1 1 1 1 1 1 10 10 10 10 39 Ice Detritus 2 2 2 2 2 40 Pelagic Detritus 1 1 1 1 1 1 1

357 Appendix F

Hudson Bay Mixed Trophic Impacts

358 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1: Mixed Trophic Impact results from balanced Hudson Bay model by species groups. Harvest or ﬁsheries are indicated by H preceding the species group or ﬁshery

Impacting / Impacted Polar SH Polar Polar Killer Narwhal Bowhead Walrus Bear Bear Bear Whale N WHB Foxe 1 Polar Bear WHB -0.5300 -0.0237 -0.0328 -0.0255 0.0138 0.0035 -0.0133 2 SH Polar Bear -0.0123 -0.5200 -0.0152 -0.0170 0.0050 0.0024 0.0007 3 Polar Bear Foxe -0.0138 -0.0111 -0.5150 -0.0123 0.0057 0.0017 0.0004 4 Killer Whale 0.0047 0.0052 0.0036 -0.5080 -0.0630 -0.0726 -0.0109 5 Narwhal -0.0006 -0.0005 -0.0007 0.0210 -0.4420 -0.0031 -0.0002 6 Bowhead 0.0000 0.0000 0.0000 0.0070 -0.0010 -0.4270 -0.0002 7 Walrus N 0.0012 -0.0002 -0.0003 0.0073 -0.0021 -0.0012 -0.4770 8 Walrus S -0.0593 -0.0585 -0.0543 -0.0119 0.0179 0.0009 0.0004 9 Bearded Seal 0.0417 0.0420 0.0752 0.0464 -0.0057 -0.0070 -0.0021 10 Harbour Seal -0.0007 -0.0007 0.0037 0.0063 -0.0022 -0.0009 -0.0001 11 Ringed Seal 0.1940 0.1970 0.1720 0.0670 -0.0671 -0.0070 -0.0035 12 Harp seal 0.0113 0.0112 0.0157 0.0114 -0.0018 -0.0016 -0.0007 13 Beluga E -0.0003 0.0010 -0.0004 0.0021 -0.0016 -0.0004 0.0001 14 Beluga W 0.0282 -0.0095 0.0194 0.0481 -0.0267 -0.0073 -0.0008 15 Beluga James -0.0013 0.0255 -0.0015 0.0189 -0.0033 -0.0028 -0.0004 16 Seabirds -0.0019 0.0102 -0.0025 -0.0018 -0.0149 0.0007 -0.0044 17 Arctic Char -0.0009 -0.0024 -0.0004 0.0008 -0.0018 -0.0006 -0.0001 18 Atlantic Salmon -0.0058 -0.0060 -0.0057 -0.0039 -0.0167 0.0003 0.0024 19 Gadiformes 0.0220 0.0181 0.0237 0.0325 0.0955 -0.0038 -0.0130 20 Sculpins/ Zoar- 0.0068 0.0059 0.0061 0.0125 0.0574 -0.0012 -0.0089 cids 21 Capelin 0.0566 0.0644 0.0603 0.0523 0.0412 -0.0131 0.0002 22 Sandlance 0.0601 0.0606 0.0555 0.0243 -0.0197 -0.0101 -0.0015 23 Sharks/Rays 0.0000 0.0000 0.0000 0.0023 -0.0008 -0.0003 -0.0001 24 Other Marine 0.0147 0.0132 0.0146 0.0165 0.0490 -0.0037 0.0149 Fish 25 Brackish Fish 0.0002 -0.0002 0.0007 0.0024 0.0058 -0.0002 0.0000 26 Cephalopods -0.0129 -0.0122 -0.0135 -0.0046 0.0182 0.0024 0.0036 27 MacroZoopl. 0.0044 0.0058 0.0042 0.0043 0.0235 -0.0113 0.0035 28 Euphausids 0.0178 0.0185 0.0169 0.0182 0.0220 0.0720 0.0030 29 Copepods 0.0125 0.0133 0.0117 0.0118 -0.0064 0.1960 -0.0045 30 Crustaceans 0.0281 0.0282 0.0346 0.0269 0.0683 -0.0015 -0.0746 31 Other Meso- -0.0128 -0.0126 -0.0138 -0.0135 -0.0239 -0.0624 0.0227 Zoopl. 32 MicroZoopl. 0.0017 0.0029 0.0018 0.0009 -0.0014 -0.0023 0.0113 33 Marine Worms -0.0124 -0.0063 -0.0046 0.0036 0.0081 -0.0028 0.0247 34 Echinoderms 0.0012 0.0060 0.0067 0.0032 0.0047 -0.0027 0.0779 35 Bivalves -0.0140 -0.0070 -0.0081 0.0027 0.0230 0.0072 0.1540 36 Other Benthos 0.0354 0.0130 0.0135 0.0110 0.0121 0.0212 -0.0155 37 Primary Produc- 0.0325 0.0351 0.0331 0.0274 0.0233 0.1130 0.0062 tion 38 Ice Algae 0.0151 0.0121 0.0131 0.0149 0.0329 0.0335 0.0183 39 Ice Detritus 0.0109 0.0064 0.0082 0.0179 0.0474 0.0158 0.1360 40 Pelagic Detritus 0.0094 0.0106 0.0100 0.0082 0.0117 0.0075 -0.0046 41 H: SH Polar Bear 0.0090 -0.3520 0.0111 0.0125 -0.0037 -0.0018 -0.0005 42 H: WHB Polar -0.2870 0.0145 0.0200 0.0155 -0.0084 -0.0022 0.0081 Bear 43 H: FB Polar Bear 0.0099 0.0079 -0.3470 0.0088 -0.0041 -0.0012 -0.0003 44 H: Killer whale -0.0047 -0.0052 -0.0036 -0.4920 0.0630 0.0726 0.0109 45 H: Bowhead 0.0000 0.0000 0.0000 -0.0052 0.0007 -0.4250 0.0001 46 H: Narwhal 0.0005 0.0004 0.0005 -0.0162 -0.4310 0.0024 0.0001 47 H: N Walrus -0.0011 0.0001 0.0002 -0.0066 0.0019 0.0010 -0.4730 48 H: S Walrus 0.0259 0.0256 0.0237 0.0052 -0.0078 -0.0004 -0.0002 49 H: Beluga E 0.0003 -0.0009 0.0003 -0.0018 0.0014 0.0003 -0.0001 50 H: Beluga W -0.0082 0.0028 -0.0057 -0.0140 0.0078 0.0021 0.0002 51 H: Beluga S 0.0002 -0.0049 0.0003 -0.0037 0.0006 0.0005 0.0001 52 H: Sealing -0.0401 -0.0406 -0.0497 -0.0268 0.0112 0.0036 0.0013 53 H: Bird Hunting 0.0000 -0.0002 0.0000 0.0000 0.0002 0.0000 0.0001 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

359 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting / Impacted Walrus S Bearded Harbour Ringed Harp Beluga Beluga Seal Seal Seal seal E W 1 Polar Bear WHB 0.0095 -0.0635 0.0009 -0.0607 -0.1130 0.0065 -0.1500 2 SH Polar Bear -0.0940 -0.0648 -0.0163 -0.0228 -0.1110 -0.0252 0.0103 3 Polar Bear Foxe 0.0045 -0.0528 -0.1840 -0.0226 -0.0682 0.0027 -0.0474 4 Killer Whale -0.0937 -0.0161 -0.1030 0.0260 -0.0261 -0.0089 -0.0381 5 Narwhal -0.0043 -0.0031 -0.0053 -0.0007 -0.0007 -0.0020 -0.0038 6 Bowhead -0.0013 -0.0003 -0.0016 0.0002 -0.0006 -0.0003 -0.0007 7 Walrus N -0.0037 -0.0017 -0.0023 0.0002 -0.0001 -0.0004 -0.0015 8 Walrus S -0.2940 0.0513 0.0931 -0.2120 0.0890 0.0104 0.0409 9 Bearded Seal -0.0177 -0.1050 -0.0922 -0.0385 -0.0765 -0.0030 -0.0274 10 Harbour Seal -0.0013 -0.0025 -0.0057 -0.0030 -0.0034 -0.0007 -0.0019 11 Ringed Seal -0.0410 -0.3140 -0.3090 -0.2760 -0.2910 -0.0363 -0.1380 12 Harp seal -0.0046 -0.0137 -0.0158 -0.0068 -0.0252 -0.0013 -0.0084 13 Beluga E -0.0006 -0.0011 -0.0009 -0.0006 -0.0013 -0.4640 -0.0010 14 Beluga W -0.0107 -0.0342 -0.0306 -0.0172 -0.0537 -0.0102 -0.2710 15 Beluga James -0.0088 -0.0053 -0.0062 -0.0018 -0.0164 -0.0025 -0.0026 16 Seabirds -0.0073 -0.0170 -0.0259 -0.0108 -0.0496 -0.0101 -0.0271 17 Arctic Char -0.0012 0.0195 -0.0138 -0.0115 -0.0123 -0.0036 0.0307 18 Atlantic Salmon 0.0019 0.0000 -0.0221 -0.0174 -0.0092 -0.0008 -0.0072 19 Gadiformes 0.0138 0.0979 -0.0229 0.0482 -0.1010 0.0284 0.0634 20 Sculpins/ Zoar- -0.0153 -0.0002 0.0074 0.0268 -0.0664 0.0413 -0.0037 cids 21 Capelin -0.0188 0.0609 0.0854 0.1170 0.7100 0.0377 0.0971 22 Sandlance -0.0122 -0.0557 0.0973 0.2150 -0.1080 -0.0132 -0.0388 23 Sharks/Rays -0.0004 -0.0001 -0.0005 0.0001 -0.0005 0.0000 -0.0002 24 Other Marine 0.0292 0.0147 0.0620 0.0408 -0.0116 -0.0061 0.0136 Fish 25 Brackish Fish -0.0008 0.0135 0.0497 -0.0069 -0.0142 0.0081 0.0245 26 Cephalopods 0.0016 -0.0191 -0.0116 -0.0382 -0.0497 0.0087 0.0102 27 MacroZoopl. 0.0005 0.0005 0.0151 0.0072 0.0413 -0.0150 0.0009 28 Euphausids -0.0028 0.0022 0.0299 0.0390 0.0836 0.0657 0.0650 29 Copepods -0.0110 -0.0237 0.0114 0.0405 0.0325 0.0422 0.0403 30 Crustaceans -0.0722 0.1450 0.0441 0.0765 0.0502 0.0572 0.0395 31 Other Meso- 0.0259 -0.0348 -0.0175 -0.0307 -0.0434 -0.0464 -0.0391 Zoopl. 32 MicroZoopl. 0.0094 -0.0120 0.0129 0.0063 0.0155 -0.0108 -0.0019 33 Marine Worms 0.0427 0.0080 0.0173 -0.0092 -0.0112 0.0158 0.0179 34 Echinoderms 0.0336 0.0618 0.0111 -0.0149 -0.0204 -0.0149 -0.0116 35 Bivalves 0.2130 0.0224 0.0208 -0.0548 0.0218 0.0018 0.0096 36 Other Benthos -0.0280 0.0363 0.0176 0.0089 -0.0418 0.0752 0.0566 37 Primary Produc- -0.0001 0.0147 0.0649 0.0873 0.1420 0.0326 0.0497 tion 38 Ice Algae 0.0236 0.0378 0.0253 0.0218 -0.0064 0.0317 0.0336 39 Ice Detritus 0.1620 0.0882 0.0455 -0.0320 -0.0332 0.0654 0.0564 40 Pelagic Detritus -0.0055 0.0129 0.0198 0.0248 0.0394 0.0075 0.0110 41 H: SH Polar Bear 0.0690 0.0475 0.0119 0.0167 0.0816 0.0185 -0.0075 42 H: WHB Polar -0.0058 0.0387 -0.0005 0.0371 0.0690 -0.0040 0.0918 Bear 43 H: FB Polar Bear -0.0032 0.0377 0.1310 0.0162 0.0487 -0.0019 0.0338 44 H: Killer whale 0.0937 0.0161 0.1030 -0.0260 0.0261 0.0089 0.0381 45 H: Bowhead 0.0010 0.0002 0.0012 -0.0002 0.0004 0.0002 0.0005 46 H: Narwhal 0.0034 0.0024 0.0041 0.0006 0.0005 0.0015 0.0029 47 H: N Walrus 0.0033 0.0016 0.0021 -0.0002 0.0001 0.0004 0.0013 48 H: S Walrus -0.3080 -0.0224 -0.0407 0.0926 -0.0389 -0.0045 -0.0179 49 H: Beluga E 0.0005 0.0010 0.0007 0.0005 0.0011 -0.4620 0.0008 50 H: Beluga W 0.0031 0.0099 0.0089 0.0050 0.0156 0.0030 -0.2120 51 H: Beluga S 0.0017 0.0010 0.0012 0.0003 0.0032 0.0005 0.0005 52 H: Sealing 0.0120 -0.2470 -0.1350 -0.0788 -0.0951 0.0060 0.0284 53 H: Bird Hunting 0.0001 0.0003 0.0004 0.0002 0.0008 0.0002 0.0004 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

360 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting / Impacted Beluga Seabirds Arctic Atlantic Gadiformes Sculpins/ Capelin James Char Salmon Zoarcids 1 Polar Bear WHB 0.0273 0.0038 0.0405 0.0142 0.0451 0.0226 0.0210 2 SH Polar Bear -0.2840 0.0003 -0.0019 0.0018 0.0106 0.0079 0.0090 3 Polar Bear Foxe 0.0120 0.0015 0.0133 0.0050 0.0173 0.0096 0.0085 4 Killer Whale -0.0383 0.0003 0.0106 0.0041 -0.0007 -0.0047 0.0008 5 Narwhal -0.0022 -0.0008 -0.0010 -0.0026 -0.0148 -0.0130 0.0001 6 Bowhead -0.0008 -0.0001 0.0002 -0.0001 0.0000 0.0000 -0.0003 7 Walrus N -0.0002 -0.0004 0.0005 0.0000 -0.0045 -0.0034 0.0010 8 Walrus S 0.0481 0.0041 -0.0130 -0.0042 0.0664 0.0629 0.0230 9 Bearded Seal -0.0278 0.0003 -0.0111 -0.0087 -0.0032 0.0063 0.0028 10 Harbour Seal -0.0014 -0.0008 0.0007 0.0002 -0.0037 -0.0050 -0.0029 11 Ringed Seal -0.1640 -0.0142 0.0469 0.0156 -0.2400 -0.2170 -0.0757 12 Harp seal -0.0135 -0.0008 0.0033 -0.0030 0.0029 0.0016 -0.0145 13 Beluga E -0.0017 -0.0004 0.0016 -0.0062 -0.0033 -0.0075 -0.0013 14 Beluga W -0.0250 -0.0107 -0.1910 -0.0609 -0.0933 -0.0091 -0.0491 15 Beluga James -0.1840 -0.0008 0.0019 -0.0019 -0.0011 0.0012 -0.0114 16 Seabirds -0.0331 -0.5210 -0.1520 -0.1660 0.0036 -0.0152 -0.0533 17 Arctic Char -0.0045 0.0083 -0.0100 -0.0801 -0.0156 -0.0190 -0.0118 18 Atlantic Salmon -0.0106 -0.0077 -0.0948 -0.1120 -0.0163 -0.0314 -0.0350 19 Gadiformes -0.0131 -0.0061 -0.0245 -0.0002 -0.1410 -0.0629 -0.0903 20 Sculpins/ Zoar- -0.0345 0.0044 -0.0049 0.0037 -0.0438 -0.1140 -0.0689 cids 21 Capelin 0.3330 0.0555 -0.0491 -0.0250 -0.0247 -0.0115 -0.0906 22 Sandlance -0.0639 0.0073 0.0301 0.0131 -0.0206 -0.0092 -0.0419 23 Sharks/Rays -0.0002 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 24 Other Marine -0.0330 0.0127 0.0073 0.0019 0.0364 0.0211 -0.0483 Fish 25 Brackish Fish -0.0084 0.0439 -0.0041 0.0047 -0.0011 0.0007 -0.0157 26 Cephalopods 0.0117 0.0305 -0.1400 -0.1010 -0.0988 -0.1610 -0.0718 27 MacroZoopl. 0.0035 0.0658 0.0123 0.0489 -0.0103 -0.0262 0.0551 28 Euphausids 0.1010 0.0299 -0.0719 0.0614 -0.0038 -0.0012 0.1200 29 Copepods 0.0589 -0.0014 0.1130 -0.0582 -0.0336 -0.0207 0.0579 30 Crustaceans 0.0565 -0.0063 0.0417 0.1380 -0.1370 -0.0784 0.0517 31 Other Meso- -0.0581 -0.0010 0.0552 -0.0626 0.0283 0.0091 -0.0556 Zoopl. 32 MicroZoopl. -0.0020 0.0196 0.0265 0.0872 -0.0123 -0.0230 0.0246 33 Marine Worms 0.0084 -0.0030 0.0059 -0.0119 0.0650 0.0619 -0.0221 34 Echinoderms -0.0245 0.0044 0.0143 -0.0122 0.0832 0.0449 -0.0129 35 Bivalves 0.0042 0.0390 -0.0390 0.0017 0.0762 0.0648 0.0045 36 Other Benthos 0.0481 -0.0024 0.0019 -0.0118 0.0637 0.0701 -0.0286 37 Primary Produc- 0.0872 0.0463 0.1560 0.1140 -0.0558 -0.0499 0.2000 tion 38 Ice Algae 0.0194 0.0165 0.0444 0.0437 0.0974 0.0602 -0.0013 39 Ice Detritus 0.0314 0.0253 -0.0139 -0.0047 0.1850 0.2260 -0.0399 40 Pelagic Detritus 0.0201 0.0353 0.0036 0.0109 -0.0148 -0.0117 0.0522 41 H: SH Polar Bear 0.2080 -0.0002 0.0014 -0.0013 -0.0077 -0.0058 -0.0066 42 H: WHB Polar -0.0167 -0.0023 -0.0247 -0.0086 -0.0275 -0.0138 -0.0128 Bear 43 H: FB Polar Bear -0.0086 -0.0011 -0.0095 -0.0036 -0.0123 -0.0068 -0.0061 44 H: Killer whale 0.0383 -0.0003 -0.0106 -0.0041 0.0007 0.0047 -0.0008 45 H: Bowhead 0.0006 0.0001 -0.0001 0.0001 0.0000 0.0000 0.0002 46 H: Narwhal 0.0017 0.0006 0.0008 0.0020 0.0114 0.0101 -0.0001 47 H: N Walrus 0.0002 0.0004 -0.0005 0.0000 0.0040 0.0031 -0.0009 48 H: S Walrus -0.0210 -0.0018 0.0057 0.0018 -0.0290 -0.0275 -0.0100 49 H: Beluga E 0.0015 0.0003 -0.0014 0.0053 0.0028 0.0065 0.0011 50 H: Beluga W 0.0073 0.0031 0.0556 0.0177 0.0271 0.0027 0.0143 51 H: Beluga S -0.1580 0.0002 -0.0004 0.0004 0.0002 -0.0002 0.0022 52 H: Sealing 0.0325 0.0020 -0.0031 0.0013 0.0322 0.0266 0.0118 53 H: Bird Hunting 0.0005 -0.0079 0.0025 0.0027 -0.0001 0.0003 0.0009 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

361 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting/Impacted Sandlance Shark/Ray Other Brackish Cephalo- Macro- Euphsiids Marine Fish pods Zoopl. Fish 1 Polar Bear WHB 0.0162 0.0348 0.0013 0.0076 0.0118 -0.0070 -0.0001 2 SH Polar Bear 0.0077 0.0169 0.0011 -0.0008 0.0020 -0.0019 -0.0005 3 Polar Bear Foxe 0.0072 0.0152 0.0012 0.0036 0.0041 -0.0027 -0.0002 4 Killer Whale -0.0105 -0.4420 -0.0006 0.0034 0.0024 -0.0004 0.0010 5 Narwhal 0.0052 -0.0176 -0.0017 -0.0021 -0.0094 0.0001 -0.0003 6 Bowhead -0.0003 -0.0064 -0.0001 -0.0001 0.0000 -0.0006 -0.0016 7 Walrus N 0.0010 -0.0084 -0.0047 0.0003 0.0000 -0.0003 -0.0004 8 Walrus S 0.0755 0.0338 0.0067 -0.0056 0.0075 -0.0061 -0.0042 9 Bearded Seal 0.0143 -0.0331 0.0033 -0.0002 0.0025 -0.0006 -0.0002 10 Harbour Seal -0.0045 -0.0078 -0.0034 -0.0064 -0.0006 0.0008 0.0003 11 Ringed Seal -0.2550 -0.1430 -0.0305 0.0195 -0.0259 0.0200 0.0136 12 Harp seal 0.0027 -0.0007 -0.0013 0.0021 0.0010 0.0024 0.0004 13 Beluga E 0.0022 -0.0034 0.0026 -0.0016 -0.0033 0.0008 -0.0006 14 Beluga W 0.0293 -0.0706 0.0080 -0.0408 -0.0419 0.0203 -0.0055 15 Beluga James 0.0014 -0.0175 0.0009 0.0011 -0.0029 0.0022 0.0000 16 Seabirds 0.0004 -0.0299 -0.0327 -0.2360 -0.1140 0.0152 -0.0017 17 Arctic Char -0.0112 0.0341 -0.0256 -0.0647 0.0129 -0.0183 0.0036 18 Atlantic Salmon 0.0024 -0.0081 -0.0387 -0.1540 -0.1280 -0.0393 0.0032 19 Gadiformes -0.1380 0.0508 -0.2560 -0.0014 0.0455 0.0292 0.0061 20 Sculpins/ Zoar- -0.1060 0.0882 -0.1310 -0.0045 0.0431 0.0189 0.0056 cids 21 Capelin -0.0592 -0.0161 -0.0219 -0.0474 0.0177 -0.1460 -0.0277 22 Sandlance -0.0898 0.0350 -0.0399 0.0006 -0.0891 -0.0386 -0.0346 23 Sharks/Rays -0.0001 -0.0495 0.0000 0.0000 0.0000 0.0000 0.0000 24 Other Marine -0.0245 0.0417 -0.0374 -0.0205 -0.0359 -0.0149 0.0051 Fish 25 Brackish Fish -0.0060 0.0295 -0.0072 -0.0487 -0.0357 -0.0193 0.0039 26 Cephalopods -0.0645 -0.0059 0.0435 0.0158 -0.1090 -0.0600 0.0206 27 MacroZoopl. -0.0313 0.0450 0.0223 0.0648 0.0915 -0.0561 -0.1970 28 Euphausids 0.0390 -0.0014 -0.0224 0.0445 -0.0190 -0.0601 -0.1080 29 Copepods 0.1340 -0.0102 0.0303 -0.1160 -0.0116 -0.1470 0.3320 30 Crustaceans 0.0109 -0.0327 0.1770 0.0907 0.0509 0.0256 -0.0682 31 Other Meso- 0.0095 0.0131 -0.0423 -0.0191 0.0513 0.0154 -0.3700 Zoopl. 32 MicroZoopl. 0.0545 0.0183 -0.0271 0.1260 0.0719 0.1600 -0.0289 33 Marine Worms -0.0180 0.0417 0.0041 -0.0126 -0.0054 -0.0413 -0.0104 34 Echinoderms -0.0118 0.0944 -0.0644 0.0063 0.0110 0.0338 -0.0276 35 Bivalves -0.0040 0.0125 0.0090 -0.0238 -0.0118 0.0267 0.0689 36 Other Benthos -0.0164 0.0049 -0.0110 0.0296 -0.0114 -0.1490 0.0276 37 Primary Produc- 0.2180 0.0265 0.1190 0.1160 0.0789 0.1910 0.1110 tion 38 Ice Algae 0.0100 0.0332 0.0772 0.0237 0.0335 0.0690 0.0318 39 Ice Detritus -0.0444 0.0836 -0.0305 0.0063 -0.0034 -0.0852 0.0428 40 Pelagic Detritus 0.0350 0.0037 0.0783 0.0494 0.0049 0.0439 0.0128 41 H: SH Polar Bear -0.0056 -0.0124 -0.0008 0.0006 -0.0015 0.0014 0.0004 42 H: WHB Polar -0.0099 -0.0212 -0.0008 -0.0047 -0.0072 0.0043 0.0001 Bear 43 H: FB Polar Bear -0.0052 -0.0109 -0.0008 -0.0026 -0.0029 0.0019 0.0001 44 H: Killer whale 0.0105 0.4420 0.0006 -0.0034 -0.0024 0.0004 -0.0010 45 H: Bowhead 0.0002 0.0047 0.0001 0.0001 0.0000 0.0005 0.0012 46 H: Narwhal -0.0040 0.0136 0.0013 0.0016 0.0073 -0.0001 0.0002 47 H: N Walrus -0.0009 0.0076 0.0043 -0.0002 0.0000 0.0003 0.0004 48 H: S Walrus -0.0330 -0.0148 -0.0029 0.0024 -0.0033 0.0027 0.0018 49 H: Beluga E -0.0019 0.0029 -0.0023 0.0014 0.0029 -0.0007 0.0005 50 H: Beluga W -0.0085 0.0205 -0.0023 0.0119 0.0122 -0.0059 0.0016 51 H: Beluga S -0.0003 0.0034 -0.0002 -0.0002 0.0006 -0.0004 0.0000 52 H: Sealing 0.0286 0.0308 0.0038 -0.0014 0.0025 -0.0029 -0.0018 53 H: Bird Hunting 0.0000 0.0005 0.0005 0.0039 0.0019 -0.0003 0.0000 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

362 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting/Impacted Copepods Crustaceans Other Micro- Marine Echino- Bivalves Meso- Zoopl. Worms derms Zoopl. 1 Polar Bear WHB 0.0001 -0.0006 0.0004 0.0009 -0.0015 -0.0047 -0.0021 2 SH Polar Bear 0.0001 -0.0001 0.0001 0.0002 -0.0005 -0.0009 -0.0003 3 Polar Bear Foxe 0.0001 -0.0002 0.0002 0.0003 -0.0006 -0.0018 -0.0009 4 Killer Whale -0.0002 0.0003 0.0000 0.0001 0.0003 0.0004 0.0005 5 Narwhal 0.0001 -0.0003 -0.0003 -0.0001 0.0009 0.0020 0.0010 6 Bowhead 0.0003 -0.0003 0.0003 0.0000 -0.0001 0.0000 0.0001 7 Walrus N 0.0002 -0.0007 0.0013 0.0000 0.0011 -0.0057 -0.0051 8 Walrus S 0.0007 -0.0021 0.0006 0.0005 -0.0038 -0.0091 -0.0059 9 Bearded Seal 0.0000 -0.0021 -0.0004 0.0001 0.0005 -0.0005 0.0007 10 Harbour Seal -0.0001 0.0002 -0.0001 -0.0001 0.0002 0.0003 0.0004 11 Ringed Seal -0.0023 0.0065 -0.0005 -0.0016 0.0139 0.0286 0.0137 12 Harp seal -0.0003 0.0008 -0.0002 -0.0002 -0.0002 -0.0006 -0.0003 13 Beluga E 0.0001 -0.0007 -0.0002 -0.0002 -0.0001 0.0010 0.0005 14 Beluga W 0.0008 0.0003 -0.0013 -0.0032 0.0002 0.0098 0.0039 15 Beluga James -0.0001 0.0001 -0.0001 -0.0002 -0.0003 0.0001 0.0001 16 Seabirds -0.0001 0.0183 0.0051 -0.0014 -0.0007 -0.0114 -0.0135 17 Arctic Char -0.0023 -0.0042 -0.0045 0.0049 -0.0015 0.0001 0.0035 18 Atlantic Salmon 0.0027 -0.0253 0.0034 0.0022 0.0067 0.0146 0.0067 19 Gadiformes -0.0007 0.0111 0.0094 -0.0028 -0.0238 -0.0756 -0.0248 20 Sculpins/ Zoar- -0.0003 -0.0077 0.0082 -0.0016 -0.0202 -0.0349 -0.0216 cids 21 Capelin 0.0155 -0.0388 0.0101 0.0109 0.0067 0.0192 0.0084 22 Sandlance 0.0033 -0.0133 -0.0172 0.0028 0.0034 0.0075 0.0035 23 Sharks/Rays 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 24 Other Marine -0.0026 -0.0509 0.0038 0.0043 -0.0068 0.0191 -0.0050 Fish 25 Brackish Fish 0.0006 -0.0070 0.0012 0.0015 0.0004 0.0008 0.0015 26 Cephalopods -0.0021 -0.0033 -0.0040 0.0100 0.0079 0.0183 0.0075 27 MacroZoopl. 0.0077 -0.0666 -0.1200 -0.1430 0.0172 0.0235 0.0019 28 Euphausids -0.3220 0.0037 -0.0605 0.1970 0.0104 0.0149 0.0024 29 Copepods -0.3750 -0.0674 -0.1680 -0.4600 0.0016 0.0178 -0.0092 30 Crustaceans -0.0194 -0.0531 0.0397 -0.0051 -0.1660 -0.3930 -0.1660 31 Other Meso- 0.0093 -0.2240 -0.0614 -0.1510 0.0389 0.0890 0.0684 Zoopl. 32 MicroZoopl. -0.0932 -0.0910 -0.0557 -0.1370 0.0204 0.0534 0.0353 33 Marine Worms 0.0004 -0.0321 -0.0303 0.0019 -0.0851 -0.0163 -0.0035 34 Echinoderms 0.0090 -0.0813 0.0519 -0.0098 -0.1710 0.0119 -0.1930 35 Bivalves -0.0202 0.0605 -0.2050 0.0015 -0.1490 -0.0520 -0.1790 36 Other Benthos -0.0038 0.0606 0.0298 0.0173 -0.1250 -0.1480 -0.2360 37 Primary Produc- 0.3210 0.0412 0.2310 0.2570 0.0085 0.0083 0.0270 tion 38 Ice Algae 0.0802 0.0762 0.1370 0.0249 0.0343 0.0039 0.0190 39 Ice Detritus -0.0147 0.1240 -0.1160 0.0088 0.2820 0.2250 0.3180 40 Pelagic Detritus 0.0105 0.0643 0.0302 0.0396 -0.0117 -0.0260 -0.0111 41 H: SH Polar Bear -0.0001 0.0001 -0.0001 -0.0001 0.0004 0.0007 0.0002 42 H: WHB Polar 0.0000 0.0003 -0.0002 -0.0006 0.0009 0.0029 0.0013 Bear 43 H: FB Polar Bear 0.0000 0.0002 -0.0001 -0.0002 0.0004 0.0013 0.0007 44 H: Killer whale 0.0002 -0.0003 0.0000 -0.0001 -0.0003 -0.0004 -0.0005 45 H: Bowhead -0.0002 0.0002 -0.0002 0.0000 0.0001 0.0000 -0.0001 46 H: Narwhal 0.0000 0.0002 0.0002 0.0000 -0.0007 -0.0016 -0.0007 47 H: N Walrus -0.0001 0.0006 -0.0012 0.0000 -0.0010 0.0051 0.0046 48 H: S Walrus -0.0003 0.0009 -0.0003 -0.0002 0.0017 0.0040 0.0026 49 H: Beluga E -0.0001 0.0006 0.0001 0.0001 0.0001 -0.0009 -0.0005 50 H: Beluga W -0.0002 -0.0001 0.0004 0.0009 -0.0001 -0.0028 -0.0011 51 H: Beluga S 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 52 H: Sealing 0.0003 -0.0003 0.0003 0.0002 -0.0020 -0.0035 -0.0020 53 H: Bird Hunting 0.0000 -0.0003 -0.0001 0.0000 0.0000 0.0002 0.0002 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

363 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting/Impacted Other Primary Ice Ice Pelagic H: SH H: WHB Benthos Produc- Algae Detritus Detritus Polar Polar tion Bear Bear 1 Polar Bear WHB 0.0002 -0.0002 0.0000 0.0012 -0.0001 -0.0237 0.4700 2 SH Polar Bear -0.0001 -0.0001 0.0000 0.0003 -0.0001 0.4800 -0.0123 3 Polar Bear Foxe 0.0001 -0.0001 0.0000 0.0005 -0.0001 -0.0111 -0.0138 4 Killer Whale 0.0000 0.0000 0.0000 -0.0003 0.0000 0.0052 0.0047 5 Narwhal 0.0001 0.0000 0.0000 -0.0006 0.0000 -0.0005 -0.0006 6 Bowhead -0.0001 -0.0001 -0.0001 0.0000 0.0000 0.0000 0.0000 7 Walrus N 0.0009 -0.0001 0.0000 0.0016 -0.0001 -0.0002 0.0012 8 Walrus S 0.0000 -0.0004 0.0001 0.0032 -0.0002 -0.0585 -0.0593 9 Bearded Seal 0.0003 0.0000 0.0001 -0.0003 0.0002 0.0420 0.0417 10 Harbour Seal 0.0000 0.0000 0.0000 -0.0002 0.0000 -0.0007 -0.0007 11 Ringed Seal 0.0011 0.0012 -0.0003 -0.0091 0.0006 0.1970 0.1940 12 Harp seal -0.0001 0.0002 0.0001 0.0001 0.0001 0.0112 0.0113 13 Beluga E -0.0001 0.0000 0.0000 -0.0001 0.0001 0.0010 -0.0003 14 Beluga W -0.0017 0.0002 0.0000 -0.0012 0.0002 -0.0095 0.0282 15 Beluga James -0.0001 0.0001 0.0001 0.0001 0.0001 0.0255 -0.0013 16 Seabirds -0.0021 -0.0004 -0.0004 0.0056 -0.0026 0.0102 -0.0019 17 Arctic Char 0.0005 0.0008 0.0012 -0.0009 0.0012 -0.0024 -0.0009 18 Atlantic Salmon 0.0033 -0.0014 -0.0011 -0.0046 0.0020 -0.0060 -0.0058 19 Gadiformes -0.0042 0.0002 0.0002 0.0178 -0.0007 0.0181 0.0220 20 Sculpins/ Zoar- -0.0020 0.0002 0.0008 0.0131 0.0005 0.0059 0.0068 cids 21 Capelin 0.0029 -0.0100 -0.0058 -0.0049 -0.0039 0.0644 0.0566 22 Sandlance 0.0020 -0.0006 0.0015 -0.0025 0.0013 0.0606 0.0601 23 Sharks/Rays 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 24 Other Marine 0.0080 0.0014 0.0010 0.0011 0.0023 0.0132 0.0147 Fish 25 Brackish Fish -0.0006 -0.0005 -0.0001 -0.0002 0.0000 -0.0002 0.0002 26 Cephalopods 0.0009 -0.0001 0.0004 -0.0050 0.0006 -0.0122 -0.0129 27 MacroZoopl. 0.0192 0.0287 0.0151 -0.0096 0.0233 0.0058 0.0044 28 Euphausids 0.0176 0.1360 0.1120 -0.0101 0.0611 0.0185 0.0178 29 Copepods -0.0331 -0.2480 -0.2130 0.0163 -0.1400 0.0133 0.0125 30 Crustaceans -0.1270 -0.0114 -0.0070 0.1230 -0.0784 0.0282 0.0281 31 Other Meso- 0.0200 -0.0257 -0.0646 -0.0357 -0.0065 -0.0126 -0.0128 Zoopl. 32 MicroZoopl. -0.0080 -0.1570 -0.0898 -0.0131 -0.1930 0.0029 0.0017 33 Marine Worms -0.3050 0.0023 0.0055 -0.0700 0.0070 -0.0063 -0.0124 34 Echinoderms -0.0420 -0.0036 0.0045 0.0713 0.0017 0.0060 0.0012 35 Bivalves -0.1090 0.0198 0.0043 -0.2260 0.0167 -0.0070 -0.0140 36 Other Benthos -0.1430 -0.0073 -0.0593 -0.1910 -0.0063 0.0130 0.0354 37 Primary Produc- -0.0259 -0.2800 -0.2300 -0.0039 -0.2550 0.0351 0.0325 tion 38 Ice Algae 0.1150 -0.0690 -0.0740 -0.0575 -0.0661 0.0121 0.0151 39 Ice Detritus 0.2730 0.0079 -0.0316 0.0000 0.0047 0.0064 0.0109 40 Pelagic Detritus -0.0117 -0.0209 -0.0167 0.0094 0.0000 0.0106 0.0094 41 H: SH Polar Bear 0.0001 0.0001 0.0000 -0.0002 0.0001 -0.3520 0.0090 42 H: WHB Polar -0.0001 0.0001 0.0000 -0.0007 0.0001 0.0145 -0.2870 Bear 43 H: FB Polar Bear -0.0001 0.0001 0.0000 -0.0004 0.0000 0.0079 0.0099 44 H: Killer whale 0.0000 0.0000 0.0000 0.0003 0.0000 -0.0052 -0.0047 45 H: Bowhead 0.0001 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 46 H: Narwhal -0.0001 0.0000 0.0000 0.0005 0.0000 0.0004 0.0005 47 H: N Walrus -0.0008 0.0001 0.0000 -0.0014 0.0001 0.0001 -0.0011 48 H: S Walrus 0.0000 0.0002 0.0000 -0.0014 0.0001 0.0256 0.0259 49 H: Beluga E 0.0001 0.0000 0.0000 0.0001 0.0000 -0.0009 0.0003 50 H: Beluga W 0.0005 -0.0001 0.0000 0.0004 -0.0001 0.0028 -0.0082 51 H: Beluga S 0.0000 0.0000 0.0000 0.0000 0.0000 -0.0049 0.0002 52 H: Sealing -0.0002 -0.0002 0.0000 0.0013 -0.0002 -0.0406 -0.0401 53 H: Bird Hunting 0.0000 0.0000 0.0000 -0.0001 0.0000 -0.0002 0.0000 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

364 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting/Impacted H: FB H: Killer H: Bow- H: Nar- H: N H: S Polar whale head whal Walrus Walrus Bear 1 Polar Bear WHB -0.0328 -0.0255 0.0035 0.0138 -0.0133 0.0095 2 SH Polar Bear -0.0152 -0.0170 0.0024 0.0050 0.0007 -0.0940 3 Polar Bear Foxe 0.4850 -0.0123 0.0017 0.0057 0.0004 0.0045 4 Killer Whale 0.0036 0.4920 -0.0726 -0.0630 -0.0109 -0.0937 5 Narwhal -0.0007 0.0210 -0.0031 0.5580 -0.0002 -0.0043 6 Bowhead 0.0000 0.0070 0.5730 -0.0010 -0.0002 -0.0013 7 Walrus N -0.0003 0.0073 -0.0012 -0.0021 0.5230 -0.0037 8 Walrus S -0.0543 -0.0119 0.0009 0.0179 0.0004 0.7060 9 Bearded Seal 0.0752 0.0464 -0.0070 -0.0057 -0.0021 -0.0177 10 Harbour Seal 0.0037 0.0063 -0.0009 -0.0022 -0.0001 -0.0013 11 Ringed Seal 0.1720 0.0670 -0.0070 -0.0671 -0.0035 -0.0410 12 Harp seal 0.0157 0.0114 -0.0016 -0.0018 -0.0007 -0.0046 13 Beluga E -0.0004 0.0021 -0.0004 -0.0016 0.0001 -0.0006 14 Beluga W 0.0194 0.0481 -0.0073 -0.0267 -0.0008 -0.0107 15 Beluga James -0.0015 0.0189 -0.0028 -0.0033 -0.0004 -0.0088 16 Seabirds -0.0025 -0.0018 0.0007 -0.0149 -0.0044 -0.0073 17 Arctic Char -0.0004 0.0008 -0.0006 -0.0018 -0.0001 -0.0012 18 Atlantic Salmon -0.0057 -0.0039 0.0003 -0.0167 0.0024 0.0019 19 Gadiformes 0.0237 0.0325 -0.0038 0.0955 -0.0130 0.0138 20 Sculpins/ Zoar- 0.0061 0.0125 -0.0012 0.0574 -0.0089 -0.0153 cids 21 Capelin 0.0603 0.0523 -0.0131 0.0412 0.0002 -0.0188 22 Sandlance 0.0555 0.0243 -0.0101 -0.0197 -0.0015 -0.0122 23 Sharks/Rays 0.0000 0.0023 -0.0003 -0.0008 -0.0001 -0.0004 24 Other Marine 0.0146 0.0165 -0.0037 0.0490 0.0149 0.0292 Fish 25 Brackish Fish 0.0007 0.0024 -0.0002 0.0058 0.0000 -0.0008 26 Cephalopods -0.0135 -0.0046 0.0024 0.0182 0.0036 0.0016 27 MacroZoopl. 0.0042 0.0043 -0.0113 0.0235 0.0035 0.0005 28 Euphausids 0.0169 0.0182 0.0720 0.0220 0.0030 -0.0028 29 Copepods 0.0117 0.0118 0.1960 -0.0064 -0.0045 -0.0110 30 Crustaceans 0.0346 0.0269 -0.0015 0.0683 -0.0746 -0.0722 31 Other Meso- -0.0138 -0.0135 -0.0624 -0.0239 0.0227 0.0259 Zoopl. 32 MicroZoopl. 0.0018 0.0009 -0.0023 -0.0014 0.0113 0.0094 33 Marine Worms -0.0046 0.0036 -0.0028 0.0081 0.0247 0.0427 34 Echinoderms 0.0067 0.0032 -0.0027 0.0047 0.0779 0.0336 35 Bivalves -0.0081 0.0027 0.0072 0.0230 0.1540 0.2130 36 Other Benthos 0.0135 0.0110 0.0212 0.0121 -0.0155 -0.0280 37 Primary Produc- 0.0331 0.0274 0.1130 0.0233 0.0062 0.0000 tion 38 Ice Algae 0.0131 0.0149 0.0335 0.0329 0.0183 0.0236 39 Ice Detritus 0.0082 0.0179 0.0158 0.0474 0.1360 0.1620 40 Pelagic Detritus 0.0100 0.0082 0.0075 0.0117 -0.0046 -0.0055 41 H: SH Polar Bear 0.0111 0.0125 -0.0018 -0.0037 -0.0005 0.0690 42 H: WHB Polar 0.0200 0.0155 -0.0022 -0.0084 0.0081 -0.0058 Bear 43 H: FB Polar Bear -0.3470 0.0088 -0.0012 -0.0041 -0.0003 -0.0032 44 H: Killer whale -0.0036 -0.4920 0.0726 0.0630 0.0109 0.0937 45 H: Bowhead 0.0000 -0.0052 -0.4250 0.0007 0.0001 0.0010 46 H: Narwhal 0.0005 -0.0162 0.0024 -0.4310 0.0001 0.0034 47 H: N Walrus 0.0002 -0.0066 0.0010 0.0019 -0.4730 0.0033 48 H: S Walrus 0.0237 0.0052 -0.0004 -0.0078 -0.0002 -0.3080 49 H: Beluga E 0.0003 -0.0018 0.0003 0.0014 -0.0001 0.0005 50 H: Beluga W -0.0057 -0.0140 0.0021 0.0078 0.0002 0.0031 51 H: Beluga S 0.0003 -0.0037 0.0005 0.0006 0.0001 0.0017 52 H: Sealing -0.0497 -0.0268 0.0036 0.0112 0.0013 0.0120 53 H: Bird Hunting 0.0000 0.0000 0.0000 0.0002 0.0001 0.0001 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Table Continued on Next Page

365 Appendix F. Hudson Bay Mixed Trophic Impacts

Table F.1 Continued Impacting/Impacted H: Beluga H: Beluga H: Sealing H: Bird H: Fishing W S Hunting 1 Polar Bear WHB -0.1500 0.0273 -0.0626 0.0038 0.0319 2 SH Polar Bear 0.0103 -0.2840 -0.0371 0.0003 0.0044 3 Polar Bear Foxe -0.0474 0.0120 -0.0330 0.0015 0.0117 4 Killer Whale -0.0381 -0.0383 0.0121 0.0003 0.0026 5 Narwhal -0.0038 -0.0022 -0.0014 -0.0008 -0.0062 6 Bowhead -0.0007 -0.0008 0.0001 -0.0001 0.0000 7 Walrus N -0.0015 -0.0002 -0.0003 -0.0004 -0.0016 8 Walrus S 0.0409 0.0481 -0.1270 0.0041 0.0271 9 Bearded Seal -0.0274 -0.0278 0.2310 0.0003 -0.0026 10 Harbour Seal -0.0019 -0.0014 0.0006 -0.0008 -0.0023 11 Ringed Seal -0.1380 -0.1640 0.3950 -0.0142 -0.0950 12 Harp seal -0.0084 -0.0135 0.0151 -0.0008 0.0006 13 Beluga E -0.0010 -0.0017 -0.0008 -0.0004 -0.0017 14 Beluga W 0.7290 -0.0250 -0.0231 -0.0107 -0.0964 15 Beluga James -0.0026 0.8160 -0.0032 -0.0008 -0.0004 16 Seabirds -0.0271 -0.0331 -0.0136 0.4790 -0.0718 17 Arctic Char 0.0307 -0.0045 -0.0025 0.0083 0.3490 18 Atlantic Salmon -0.0072 -0.0106 -0.0122 -0.0077 -0.0440 19 Gadiformes 0.0634 -0.0131 0.0587 -0.0061 0.1300 20 Sculpins/ Zoar- -0.0037 -0.0345 0.0167 0.0044 0.1560 cids 21 Capelin 0.0971 0.3330 0.1150 0.0555 0.0651 22 Sandlance -0.0388 -0.0639 0.1280 0.0073 0.0269 23 Sharks/Rays -0.0002 -0.0002 0.0000 0.0000 0.0000 24 Other Marine 0.0136 -0.0330 0.0320 0.0127 0.0586 Fish 25 Brackish Fish 0.0245 -0.0084 -0.0010 0.0439 0.0161 26 Cephalopods 0.0102 0.0117 -0.0329 0.0305 -0.1130 27 MacroZoopl. 0.0009 0.0035 0.0061 0.0658 0.0047 28 Euphausids 0.0650 0.1010 0.0294 0.0299 -0.0131 29 Copepods 0.0403 0.0589 0.0216 -0.0014 0.0388 30 Crustaceans 0.0395 0.0565 0.0955 -0.0063 -0.0114 31 Other Meso- -0.0391 -0.0581 -0.0322 -0.0010 0.0192 Zoopl. 32 MicroZoopl. -0.0019 -0.0020 0.0012 0.0196 0.0087 33 Marine Worms 0.0179 0.0084 -0.0042 -0.0030 0.0255 34 Echinoderms -0.0116 -0.0245 0.0073 0.0044 0.0268 35 Bivalves 0.0096 0.0042 -0.0303 0.0390 0.0155 36 Other Benthos 0.0566 0.0481 0.0156 -0.0024 0.0250 37 Primary Produc- 0.0497 0.0872 0.0675 0.0463 0.0724 tion 38 Ice Algae 0.0336 0.0194 0.0258 0.0165 0.0541 39 Ice Detritus 0.0564 0.0314 0.0031 0.0253 0.0736 40 Pelagic Detritus 0.0110 0.0201 0.0217 0.0353 0.0075 41 H: SH Polar Bear -0.0075 0.2080 0.0272 -0.0002 -0.0032 42 H: WHB Polar 0.0918 -0.0167 0.0382 -0.0023 -0.0195 Bear 43 H: FB Polar Bear 0.0338 -0.0086 0.0236 -0.0011 -0.0084 44 H: Killer whale 0.0381 0.0383 -0.0121 -0.0003 -0.0026 45 H: Bowhead 0.0005 0.0006 0.0000 0.0001 0.0000 46 H: Narwhal 0.0029 0.0017 0.0011 0.0006 0.0048 47 H: N Walrus 0.0013 0.0002 0.0003 0.0004 0.0014 48 H: S Walrus -0.0179 -0.0210 0.0556 -0.0018 -0.0119 49 H: Beluga E 0.0008 0.0015 0.0007 0.0003 0.0015 50 H: Beluga W -0.2120 0.0073 0.0067 0.0031 0.0280 51 H: Beluga S 0.0005 -0.1580 0.0006 0.0002 0.0001 52 H: Sealing 0.0284 0.0325 -0.1280 0.0020 0.0134 53 H: Bird Hunting 0.0004 0.0005 0.0002 -0.0079 0.0012 54 H: Fishing 0.0000 0.0000 0.0000 0.0000 0.0000

366 Appendix G

Hudson Bay Monte Carlo CV Values

367 Appendix G. Hudson Bay Monte Carlo CV Values

Table G.1: Coeﬃcient of Variation (CV) values used for Monte Carlo routine; Biomass, Production/Biomass (P/B), Ecotrophic Eﬃciency (EE) and Biomass Accumulation (BA).

Functional Group Biomass (CV) P/B (CV) EE (CV) BA (CV) Polar Bear WHB 0.15 0.25 0.1 0.05 SH Polar Bear 0.15 0.25 0.1 0.05 Polar Bear Foxe 0.15 0.25 0.1 0.05 Killer Whale 0.15 0.1 0.1 0.05 Narwhal 0.15 0.1 0.1 0.05 Bowhead 0.4 0.1 0.1 0.15 Walrus N 0.25 0.1 0.1 0.05 Walrus S 0.25 0.1 0.1 0.05 Bearded Seal 0.25 0.1 0.1 0.05 Harbour Seal 0.25 0.1 0.1 0.05 Ringed Seal 0.25 0.1 0.1 0.05 Harp seal 0.25 0.1 0.1 0.05 Beluga E 0.15 0.1 0.1 0.15 Beluga W 0.15 0.1 0.1 0.15 Beluga James 0.15 0.1 0.1 0.05 Seabirds 0.4 0.3 0.1 0.05 Arctic Char 0.1 0.2 0.1 0.05 Atlantic Salmon 0.1 0.2 0.1 0.05 Gadiformes 0.1 0.2 0.1 0.05 Sculpins/ Zoarcids 0.1 0.2 0.1 0.05 Capelin 0.1 0.2 0.1 0.05 Sandlance 0.1 0.2 0.1 0.05 Sharks/Rays 0.1 0.2 0.1 0.05 Other Marine Fish 0.1 0.2 0.1 0.05 Brackish Fish 0.1 0.25 0.1 0.05 Cephalopods 0.25 0.3 0.1 0.05 MacroZooplankton 0.25 0.2 0.1 0.05 Euphausids 0.15 0.2 0.1 0.05 Copepods 0.15 0.2 0.1 0.05 Crustaceans 0.15 0.2 0.1 0.05 Other MesoZooplankton 0.15 0.2 0.1 0.05 MicroZooplankton 0.25 0.35 0.1 0.05 Marine Worms 0.1 0.25 0.1 0.05 Echinoderms 0.1 0.25 0.1 0.05 Bivalves 0.1 0.25 0.1 0.05 Other Benthos 0.1 0.25 0.1 0.05 Primary Production 0.15 0.1 0.1 0.05 Ice Algae 0.15 0.1 0.1 0.05

368 Appendix H

Hudson Bay Monte Carlo Results

369 370 iueH1 ot al eut o siae fboas( biomass of estimates for results Carlo Monte H.1: Figure

Biomass 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Polar Bear WHB

Polar Bear SH

Polar Bear Foxe

Killer Whale

Narwhal

Bowhead

Walrus N

Walrus S

Bearded Seal

Harbour Seal

Ringed Seal

Harp seal

Belgua E

Belgua W

Beluga James t

· P/B km 0.00 0.05 0.10 0.15 0.20 0.25 0.30 − 2 n / ( P/B and ) Polar Bear WHB

Polar Bear SH

Polar Bear Foxe

Killer Whale y

− Narwhal 1 o aiemma groups. mammal marine for ) Bowhead

Walrus N

Walrus S

Bearded Seal

Harbour Seal

Ringed Seal

Harp seal

Belgua E

Belgua W

Beluga James 371

Biomass 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 iueH2 ot al eut o siae fboas( biomass of estimates for results Carlo Monte H.2: Figure

Seabirds

Arctic Char

Atlantic Salmon

Gadiformes

Sculpins/ Zoarcids

Capelin

Sandlance

Sharks/Rays

Other Marine Fish

Brackish Fish

P/B 0 1 2 3 4 5 6 t ·

km Seabirds − 2

n / ( P/B and ) Arctic Char

Atlantic Salmon

Gadiformes y

− Sculpins/ Zoarcids 1 o s groups. ﬁsh for )

Capelin

Sandlance

Sharks/Rays

Other Marine Fish

Brackish Fish 372 iueH3 ot al eut o siae fboas( biomass of estimates for groups. results producer Carlo Monte H.3: Figure

Biomass 10 11 12 0 1 2 3 4 5 6 7 8 9

Cephalopods

MacroZoopl.

Euphausids

Copepods

Crustaceans

Other MesoZoopl.

MicroZoopl.

Marine Worms

Echinoderms

Bivalves

Other Benthos

Primary Production

Ice Algae t

· P/B km 10 20 30 40 50 60 − 0 2 n / ( P/B and )

Cephalopods

MacroZoopl.

Euphausids y

− Copepods 1 o opako n primary and zooplankton for ) Crustaceans

Other MesoZoopl.

MicroZoopl.

Marine Worms

Echinoderms

Bivalves

Other Benthos

Primary Production

Ice Algae Appendix I

Hudson Bay Ecosim Biomass Trends by Species

373 Appendix I. Hudson Bay Ecosim Biomass Trends by Species

3.0 Polar Bear WHB SH Polar Bear Polar Bear Foxe Killer Whale Narwhal 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Bowhead Walrus N Walrus S Bearded Seal Harbour Seal 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Ringed Seal Harp seal Beluga E Beluga W Beluga James 2.5 2.0

Relative Biomass Relative 1.5 1.0 0.5 0.0

3.0 Seabirds Arctic Char Atlantic Salmon Gadiformes Sculpins/ Zoarcids 2.5 2.0 1.5 1.0 0.5 0.0

1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 2010 Year

Figure I.1: Biomass trends from 1970-2010 as scaled to 1970 biomass value by functional group

374 Appendix I. Hudson Bay Ecosim Biomass Trends by Species

3.0 Capelin Sandlance Sharks/Rays Other Marine Fish Brackish Fish 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Cephalopods MacroZooplankton Euphausids Copepods Crustaceans 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Other MesoZoopl. MicroZoopl. Marine Worms Echinoderms Bivalves 2.5 2.0

Relative Biomass Relative 1.5 1.0 0.5 0.0

3.0 Other Benthos Primary Production Ice Algae Ice Detritus Pelagic Detritus 2.5 2.0 1.5 1.0 0.5 0.0

1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 2010 Year

Figure I.2: Biomass trends from 1970-2010 as scaled to 1970 biomass value by functional group

375 Appendix J

Antarctic Peninsula Ecosystem Model Parameters and Details

J.1 Model Parameters by Functional Group

Functional groups were created with a focus on krill, salps, and the top predators primarily dependent on krill. Marine mammals and penguins were given functional groups for each species identiﬁed in the model area, while ﬁsh were grouped together based on life history and diet. Pelagic and benthic surveys provided information on invertebrate species, therefore allowing for greater detail of these species groups.

Marine Mammals

Table J.1: Estimates of cetaceans from three Southern Ocean circumpolar surveys as presented in Branch and Butterworth (2001).

Species First Circumpolar Second Circumpolar Third Circumpolar (1978-1984) (1985-1991) (1991-1998) Blue Whale 440 550 1100 Fin Whale 2100 2100 5500 Sperm Whale 5400 10000 8300 Humpback 7100 9200 9300 Whale Killer Whale 91000 27000 25000

The marine mammals included in this model are the species which have been identiﬁed to inhabit the region on a yearly basis, or make a seasonal

376 J.1. Model Parameters by Functional Group migration back to the region every year. For the purpose of the model the marine mammal groups each represent an individual species. Biomass for each group was determined by using the average weight per individual as determined by Trites and Pauley (1998), compared to distribution and abundance information for each species. Southern Ocean abundance of cetacean species are found in table J.1 as summarized by Branch and Butterworth (2001). Mortality rates were calculated for each species using the life table from Barlow and Boveng (1991), where life history patterns and longevity are used to calculate natural mortality. Full equations are available in appendix B, with longevity listed in table J.2. These values were used as the P/B ratio for the first year of the model, as there is no hunting mortality on marine mammal species within the model area. Consumption (Q/B) was calculated using an empirical equation (Eq. J.1) from Hunt et al. (2000), where E in the energy required per day (Kcal/day), M is the mean body weight (in Kg) and a is a coefficient representing each group of marine mammals (a=320 for otariids, 200 for phocids, 192 for mysticetes, 317 for odontocetes, and 320 for sea otters). The energy requirement was compared to energy consumed, based on energy content in the diet (Cauffope and Heymans, 2005), in order to get the Q/B ratio. Table J.2 shows calculated values, values from literature, and values used in the model. E = aM 0.75 (J.1)

377 Table J.2: Published and calculated marine mammal parameters used in the model.

Functional Group Mean Longevity Reference Natural Model Calc. Model Weight (Years) Mortality‡ P/B Q/B∗ Q/B (Kg) 1 Killer Whales 2280.5† 50 Trites and Pauley (1998) 0.057 0.05 7.39 11 2 Leopard Seal 464 26 Australian Antarctic Divi- 0.102 0.12 9.95 8.1 sion (2008) 3 Ross Seal 145.5 24 Skinner and Klages (1994) 0.125 0.13 15.3 15.3 4 Weddell Seal 158 13.5† Trites and Pauley (1998) 0.175 0.17 13.88 13.88 5 Crabeater Seal 206 36 Carey and Judge (2001) 0.083 0.09 15.86 15.86 6 Antarctic Fur Seals 26.7† 13.5 Trites and Pauley (1998) 0.175 0.175 33.18 25 7 Elephant Seals 435† 15 Trites and Pauley (1998) 0.165 0.165 10.37 10.37 8 Sperm Whales 18518.5† 69 Trites and Pauley (1998) 0.043 0.034 7.33 7.33 9 Blue Whales 102736.5† 100 Trites and Pauley (1998) 0.032 0.032 3.53 3.53 10 Fin Whales 55590† 98 Trites and Pauley (1998) 0.035 0.035 4.12 4.12 11 Minke Whales 6566† 47 Trites and Pauley (1998) 0.064 0.064 6.34 6.34 12 Humpback Whales 30408† 75 Trites and Pauley (1998) 0.04 0.04 4.54 4.12 † Mean weights taken from Trites and Pauley (1998) were averaged for males and females. ‡ Natural mortality was calculated from Barlow and Boveng (1991), see appendix B for equations. ∗ Calculated Q/B values were obtained using Eq.J.1. 378 J.1. Model Parameters by Functional Group

Killer Whales

(Orcinus orchus) Recently in the Antarctic, three ecotypes have been identified which appear to be similar to the killer whales residing in the Pacific Northwest. In the Antarctic Peninsula types A (minke whale specialists) and B (seal specialists) have been observed, while type C is primarily observed off east Antarctica and has been observed to consume Antarctic toothfish; Dis- sostichus mawsoni (Pitman and Ensor, 2003; Waples and Clapham, 2004). For this model it is assumed that killer whales are year round inhabitants based on observations in the winter at other locations in the Antarctic (Gill and Thiele, 1997). Based on surveys in table J.1 from Branch and Butter- worth (2001), the biomass from the first, second and third surveys would have been 0.0058, 0.0017, 0.0016 t · km−2, assuming an even distribution of whales in the Southern Ocean. However, more localized surveys present much lower estimates of killer whales (Childerhouse, 2005; Secchi et al., 2006).The biomass for the first year was set to 0.001 t · km−2. The production/ biomass ratio was calculated to be 0.057y−1, but was lowered to 0.05y−1 to balance the model. The Q/B ratio was set to 11y−1, which is higher than the calculated transient Orca value in Guenette (2005). How- ever it was used being that the majority of whales sighted in the area fed on marine mammals (Pitman and Ensor, 2003). Based on observations of marine mammal eating killer whales, the diet was set to: 4% leopard seals, 2% Ross seals, 16% Weddell seals, 19% crabeater seals, 9% Antarctic fur seals, 0.1% blue whales, 0.5% fin whales, 34.4% minke whales, 7% humpback whales, 6% penguins (1% Gentoo, 2% Chinstrap, 1% Macaroni, 3% Adelie), and 1% flying birds.

Leopard Seal

(Hydrurga leptonyx) Leopard seals are year round inhabitants, with their diet being dominated by krill and ﬁsh in the winter then shifting to penguins and other marine mammals in the summer (Lowry et al., 1998; Walker et al., 1998; Hall-Aspland and Rogers, 2004). Estimates of leopard seals range from 0.5-1.1 seals·km−2 for the Weddell Sea (Van Franeker et al., 1997),

379 J.1. Model Parameters by Functional Group to 0.1 seals·km−2 for the Amundsen and Bellingshausen Seas (Gilbert and Erickson, 1977). The biomass for the model was set to 0.00576t·km−2. This is lower than other areas, as leopard seals are associated with pack ice, and the other areas sampled have higher levels of year round sea ice. The P/B was increased slightly from the calculated value to 0.12 y−1 to account for killer whale predation, and the Q/B value was lowered slightly to 8.1 −1 to balance the penguin groups. The average diet was set to: 0.5% Ross seal, 1.5% Weddell seal, 7% crabeater seal, 7% Antarctic fur seal, 1% elephant seal, 1.5% emperor penguins, 1% Gentoo penguins, 1% chinstrap penguins, 3% macaroni penguins, 10% Adelie penguins, 4% flying birds, 15% cephalopods, 2% other icefish, 1% large notothenioids, 1% small notothenioids, 1% shallow demersals, 0.5% deep demersals large, 1% deep demersals small, 1% myctophids, 1% other pelagics, 1% C. gunnari, 1% P. antarcticum, 1% N. gibberifrons, 19% adult krill, and 17% sub-adult krill (Penney and Lowry, 1967; Muller-Schwarze and Muller-Schwarze, 1975; Siniff and Stone, 1985; Walker et al., 1998; Hiruki et al., 1999; Hall-Aspland and Rogers, 2004).

Ross Seal

(Ommatophoca rossii) The Ross seal lives deep within the pack ice and is one of the least studied seals. They are known to feed primarily on ﬁsh and squid, with dive depths mostly correlating to pelagic feeding with some benthic dives (Skinner and Klages, 1994; Bengston and Stewart, 1977). They are mostly found in interior pack ice zones in places such as the Ross Sea; 0.6 animals·km−2 Ackley et al. (2003), with a smaller portion found in the pelagic areas (Gilbert and Erickson, 1977). The biomass for the peninsula was set to nearly half of the Ross Sea population, or 0.0042t · km−2. The P/B ratio was set to the calculated value 0.13y−1, and the calculated value of 15.3y−1 was used for Q/B. The diet for Ross seals was set to: 46% cephalopods, 1.5% other iceﬁsh, 4.5% large notothenioids, 1% small notothenioids, 0.1% shallow demersals, 0.1% deep demersals large, 0.5% deep demersals small, 2% myctophids, 1%

380 J.1. Model Parameters by Functional Group other pelagics, 2% C. gunnari, 14% P. antarcticum, 3% N. gibberifrons, 4% mollusca, 1.5% salps, 0.5% cnidarians, 2.9% arthropod crustacea, 0.5% other arthropods, 1% worms, 6% adult krill, and 7.9% sub adult krill (Knox, 1994; Skinner and Klages, 1994; Casaux et al., 1997).

Weddell Seal

(Leptonychotes weddellii) Weddell seals have a circumpolar distribution and are known to inhabit the pack or fast ice near the continent, and haul out on the islands near the peninsula (Riffenburg, 2006). Biomass for the Southern Ocean averaged 0.005t · km−2 (Laws, 1977), and 0.021 to 0.12t · km−2 for the Amundsen and Bellingshausen Seas for the 1970’s and 1994 respectively (Gilbert and Erickson, 1977; Gelatt and Siniff, 1999). The biomass was set to 0.021t · km−2 for the model. The P/B was increased from the calculated value to 0.17y−1 to account for predation by killer whales, and the Q/B was set to the calculated value of 13.88y−1. The diet of Weddell seals contains cephalopods ranging from 2-65%, molluscs 1-65%, and crustaceans 2-23%, with various fish and cephalopods contributing greatly to their diet (Clarke and MacLeod, 1982; Green and Bur- ton, 1987; Casaux et al., 1997; Burns et al., 1998). The diet composition was set to: 29% cephalopods. 0.5% other icefish, 3% large notothenioids, 1.5% small notothenioids, 0.1% deep demersals large, 0.5% deep demersals small, 2% myctophids, 1% other pelagics, 2% C. gunnari, 23% P. antarcticum, 3% N. gibberifrons, 18% mollusca, 6.4% arthropod crustaceans, 0.5% other arthropods, 1% worms, 3.5% adult krill, 5% sub-adult krill (Green and Bur- ton, 1987; Casaux et al., 1997; Burns et al., 1998).

Crabeater Seal

(Lobodon carcinophagus) Crabeater seals are generally found within the pack ice and are the most abundant pinniped species in the Antarctic (Riﬀenburg, 2006). Although crabeater seals have been known to consume some ﬁsh, they feed almost exclusively on krill, demonstrating a specialized adaptation in their teeth to strain the water from large mouthfuls of krill (Lowry et al.,

381 J.1. Model Parameters by Functional Group

1998). Estimates of crabeater seals from the Amundshausen and Belling- shausen Seas are as high as 3.32 seals·km−2 in 1994 (Gelatt and Siniﬀ, 1999), with estimates on pack ice averaging 0.76 seals·km−2 from the 1970s (Gilbert and Erickson, 1977). The density of seals in the pack ice in the Weddell Sea ranged from 0.45 to 1 seal·km−2 (Van Franeker et al., 1997). A density of 0.8 seals·km−2 or 0.164t · km−2 was used for the Antarctic Peninsula. The P/B for crabeater seals was increased from the calculated value of 0.083 to 0.09y−1 to balance the model, and the Q/B was set to the calculated value of 15.86y−1. The diet for crabeater seals was set to: 2.5% cephalopods, 0.5% myctophids, 0.25% other pelagics, 2% P. antarcticum, 3% mollusca, 1% salps, 45% adult krill, 40% sub-adult krill, 5% macro-zooplankton, 0.7% micro-zooplankton (Lowry et al., 1998; Bredesen, 2003; Efran and Pitcher, 2005).

Antarctic Fur Seals

(Arctocephalus gazella) There is a large proportion of fur seals within the Scotia Sea as South Georgia is one of the main breeding grounds. However, seals do travel between South Georgia and the Peninsula, and there are a number of seals which do breed near the peninsula (Boyd et al., 1998). The biomass at South Georgia was estimated to be just over 1 million seals in the 1980s (Doidge and Croxall, 1985) or 0.028·km−2. The same value was used for the model biomass. The P/B ratio used as calculated from the life table, 0.175y−1, was comparable to the estimate 0.16y−1 for northern fur seals (Wikens and York, 1997; Guenette, 2005). The calculated Q/B of 33.18y−1 was lowered to 25y−1 to reduce the predation mortality on krill and fish species. Antarctic fur seals primarily consume krill, with fish being an important food source to males during the winter. Yearly estimates of fish contribution to the diet from a variety of species ranges from 5-50% at South Georgia Doidge and Croxall (1985). North et al. (1983), Reid (1995), and Reid and Arnould (1996) provide individual species contribution of fish to the diet. Cephalopods in the diet at South Georgia average 12% a year, with krill esti-

382 J.1. Model Parameters by Functional Group mates as high as 92% (Doidge and Croxall, 1985; Daneri and Carlini, 1999). The average yearly diet was set to: 18% cephalopods, 1% other iceﬁsh, 2.4% large notothenioids, 1% small notothenioids, 0.1% shallow demersals, 0.1% deep demersals large, 0.25% deep demersals small, 1% myctophids, 1% other pelagics, 2.5% C. gunnari, 3% N. gibberifrons, 34.8% adult krill. 34.9% sub- adult krill (North et al., 1983; Doidge and Croxall, 1985; Reid, 1995; Reid and Arnould, 1996)

Southern Elephant Seals

(Mirounga leonina) The Southern elephant seal, the largest of the Antarc- tic seals, is capable of diving up to 900 meters in order to forage for food that is mostly comprised of cephalopods (McConnell et al., 1992). Popula- tion size before the 1960s was noted as 315,100 seals for the area including South Georgia, Falkland Islands, Patagonia, South Shetland Island, Bou- vet Island, and Gough Island (Laws, 1960). This accounts for almost half of the total estimated population for the Southern Ocean at 600,000 (Laws, 1977) or 0.0026t·km−2, assuming equal distribution. Later studies from Ele- phant Island (South Shetland Islands) indicated only 300 animals resided on the island (Hunt, 1973). The biomass for the model area was set to 0.00647·km−2, assuming 10,000 of the 315,100 seals from the 1960s were located in the model area. The P/B and Q/B calculated values of 0.165y−1 and 10.37y−1 respectively were used for the model. Dive profiles of elephant seals indicate benthic feeding, with shallower dives translating to travel time between feeding grounds (McConnell et al., 1992). However, fish in the diet at King George Island at the Antarctic Peninsula is dominated by myctophids; a partially pelagic species, followed by notothenioids and icefish (Daneri and Carlini, 2002). A higher proportion of seals have a fish dominated diet in the winter, indicative of shelf foraging, while squid dominate the diets in the summer, indicative of pelagic foraging (Bradshaw et al., 2003). Overall, squid are the most important contributor to the diet at South Georgia (Rodhouse et al., 1992). The diet composition was set to: 72% cephalopods, 1.5% other icefish, 1.5% toothfish, 0.1% large

383 J.1. Model Parameters by Functional Group notothenioids, 8% myctophids, 1% other pelagics, 2% P. antarcticum, 7.9% mollusca, 1% arthropod crustaceans, 3% adult krill, and 2% sub-adult krill (McConnell et al., 1992; Rodhouse et al., 1992; Daneri and Carlini, 2002; Bradshaw et al., 2003).

Sperm Whales

(Physeter macrocephalus) Early estimates by Laws (1977) suggested the sperm whale population in the Southern Ocean was roughly 43,000 whales, however more recent studies have estimated the population to be in the 5,400-10,000 range below 60· S (Branch and Butterworth, 2001). Whether these are differences in sampling or changes to the population remain unknown. Density at South Georgia ranged from 0.00013 to 0.00019 whales·km−2 for 1999 and 2000 respectively (Leaper et al., 2000), leading to a biomass of 0.0024 to 0.0035t·km−2. Biomass was assumed to be higher at the peninsula than South Georgia due to cooler deeper waters and was set to 0.005t·km−2 for the start of the model. The P/B was lowered from the calculated value of 0.043y−1 to 0.034y−1 to balance the model. The calculated Q/B value of 7.33y−1 was used. The diet of sperm whales is thought to be based in deep water to coincide with their ability to dive at depths for long periods of time. Squid makes up the majority of the diet, with fish and invertebrates taken opportunistically (Knox, 1994; Pauly et al., 1998b). Based on this information the diet was set to: 75.2% cephalopods, 1.5% toothfish, 1% deep demersals large, 2% deep demersals small, 3% mollusca, 2% salps, 0.5% hemichordata, 0.5% brachiopoda, 0.5% bryozoa, 1.5% cnidarians, 0.1% crustaceans, 1% worms, 0.4% holothuroidea, 5.5% adult krill, and 5.3% sub-adult krill.

Baleen Whales

For the 4 groups of baleen whales in the model, adjustments have been made to the peak summer biomass in order to correct for the fact that these animals do not inhabit the model area year round. During the summer months, these whales migrate great distances in order to feed on shifting

384 J.1. Model Parameters by Functional Group populations of krill and then travel to their winter breeding grounds. Most species feed in the Antarctic Peninsula region for only three to six months per year, but their impact on the ecosystem is related to their food intake, and is not strictly proportional to the amount of time spent in the area. Growth of baleen plates and trophic signatures have been correlated with feeding time in high latitude areas for southern right whales (Best and Schell, 1996). This study demonstrates how important the summer feeding season is to the growth of baleen whales, and how it accounts for a majority of the food consumed annually by these animals. However, bowhead whales in the northern hemisphere, especially juveniles, have been shown to feed heavily in summer and winter indicating they require food sources outside of their summer feeding areas (Schell et al., 1989). In order to account for the fact that most, but not all of their annual food intake comes from the peninsula, the biomass of the baleen whales has been adjusted to be 75% of their peak summer biomass.

Blue Whales (Balaenoptera musculus) Blue whales migrate to the peninsula every austral summer in order to take advantage of the high krill biomass, which accounts for most of their annual food intake. Branch and Butterworth (2001) estimated the population in the Southern Ocean to be between 400-1100 whales based on three surveys taken over a 20 year period. However, the CCAMLR survey in 2000 only recorded 1 blue whale in the survey area of the peninsula region (Reilly et al., 2004), which did not include the entire model area. Based on the survey data, it was assumed an average of ﬁve whales would be present in the model area for the summer feeding months. The adjusted biomass19 for blue whales is 0.0005t · km−2. The P/B and Q/B were set to the calculated values in table J.2. Blue whale diets consists of small amounts of cephalopods, myctophids, with large amounts of krill (Laws, 1977; Armstrong and Siegfried, 1991; Knox, 1994; Tamura and Konishi, 2005). The diet for blue whales was assumed to be: 3% myctophids, 2% other pelagics, 3% P. antarcticum, 35% adult krill, 35% sub-adult krill, 2% macro-zooplankton, 5% micro-

19Assuming 75% of the biomass for 5 whales

385 J.1. Model Parameters by Functional Group zooplankton and 15% copepods.

Fin Whales (Balaenoptera physalus) Fin whales are also only present in the model area during the summer. Estimates for the Southern Ocean range from 2100 to 5500 whales (Branch and Butterworth, 2001), with 56 whales estimated to be in the peninsula region (Reilly et al., 2004). For the model it was estimated that 50 whales inhabit the peninsula region for the summer months, giving a yearly average biomass of 0.003t · km−2. The calculated P/B and Q/B values from table J.2 were used for the model. The diet for ﬁn whales was set to a diet similar to blue whales in the area as they are believed to primarily target krill while likely consuming a small amount of ﬁsh and other organisms. Average annual diet was set to: 5% myctophids, 2% other pelagics, 3% P. antarcticum, 39% adult krill, 30% sub-adult krill, 5% macro-zooplankton, 6% micro-zooplankton and 10% copepods.

Minke Whales (Balaenoptera bonaerensis) The summering population of minke whales has been shown to range from 112 whales (Reilly et al., 2004) to 1544 whales for areas between South America and the Antarctic Peninsula (Williams et al., 2006). Abundance estimates for the western Weddell Sea were 0.04 whales·km−2 for areas south of the ice edge, with no whales found in areas north of the ice edge (Van Franeker et al., 1997). Based on this literature, the biomass of minke whales was set to 0.011t · km−2 or 1500 whales present during the summer. This value was increased slightly to give a biomass of 0.065t · km−2 in order to balance the model, as there are a large component of killer whales in the area. The calculated P/B and Q/B values were used for the model from table J.2. The diet for minke whales was set to: 0.5% cephalopods, 0.1% myctophids, 0.5% other pelagics, 0.5% P. antarcticum, 20% adult krill, 45% sub-adult krill, 5% macro-zooplankton, 15% micro-zooplankton and 13.4% copepods.

Humpback Whales (Megaptera novaeangliae) The population of humpback whales in the Southern ocean was estimated to be between 7100-9300 whales for the years 1978 to 1998 (Van Franeker et al., 1997). Based on

386 J.1. Model Parameters by Functional Group migrations of humpback and photo identification 1105 individual whales have been identified between their summer feeding grounds at the Antarctic Peninsula and their breeding grounds in western South America (Ecuador and Columbia) and Brazil, with 535 individuals sighted within the model area (Stevick et al., 2004). Others estimate 181 whales between the peninsula and South Georgia (Reilly et al., 2004), however this survey did not include all of area 48.1. The biomass for humpback whales was set to 0.02t · km−2 which is based on 600 whales residing in the model area for the summer months. The calculated P/B of 0.04y−1 was used, however the Q/B was decreased slightly from 4.54y−1 to 4.12y−1 to balance the model. In the summer, krill is a main staple of the diet, contributing up to 97% of the diet, with cephalopods and fish making up the rest (Laws, 1977; Knox, 1994). The diet was set to: 0.5% cephalopods, 1% myctophids, 0.5% other pelagics, 0.5% P. antarcticum, 37.5% adult krill, 35% sub-adult krill, 5% macro-zooplankton, 10% micro-zooplankton and 10% copepods.

Penguins

There are ﬁve species of penguins known to reside in the Antarctic Penin- sula; the adelie, gentoo, chinstrap, emperor, and macaroni penguins. The emperor and adelie penguins have a circumpolar distribution, while, gentoo, macaroni and chinstrap penguins are found on sub-Antarctic islands and the peninsula is generally the only portion of the continent in which they reside. At PALMER station on Anvers Island, a US monitoring program has noted that before 1950 only adelie penguins were known to inhibit the region. Adelie populations are believed to have increased from the 1950s to the 1970s, when populations at certain areas began to decline (King George Island and Signy Island) (Croxall et al., 2002). However, since this time gentoo and chinstrap penguins have moved south in correlation with warming trends (Emslie et al., 1998). The gentoo population at Cierva Point on the peninsula has nearly doubled from 1954 to 1996 (Quintana and Cirelli, 2000). While it is unknown if these trends hold true across the entire model area, data from the PALMER station Long Term Ecological Research (LTER)

387 J.1. Model Parameters by Functional Group

700 16000

600 14000

12000 500

10000 400 Chinstrap Gentoo 8000 300 Adelie 6000

breeding pairs 200 4000

Number Number Adelie breeding pairs 100 2000

Number Number of Gentoo andChinstrap

0 0 1975 1979 1983 1987 1991 1995 1999 2003 Year

Figure J.1: Number of breeding pairs for Chinstrap, Gentoo, and Adelie penguins from surveys at PALMER station, Anvers Island, Antarctic Peninsula (Fraser, 2006). dataset has been incorporated into the model (Figure J.1). Surveys of various penguin rookeries at the south Shetland islands estimate populations of adelie, gentoo, and chinstraps at 65300, 12600, and 625000 breeding pairs respectively for the early 1980s (Trivelpiece et al., 1987)20. These were considered conservative estimates, as the surveys did not cover the entire model area. A more comprehensive survey for adelie penguins identiﬁed higher abundance within inshore zones in 1986; up to 3.5 animals·km−2 (Whitehouse and Viet, 1994). Abundances of macaroni and emperor penguins were assumed based on their relative abundance compared to other penguin species, primarily adelie P/B ratios were assumed for most species, based on survival rates of adelie penguins and taking into account other factors such as size and longevity. An average mortality rate of 0.29y−1 was used based for adelie penguins on survivorship from 1968-

20It should be noted that Trivelpiece et al. (2010) identify declines of all three species of penguins studied: Adelie, Chinstrap and Gentoo in a recent study and is compared with the model in the discussion.

388 J.1. Model Parameters by Functional Group

1976 at colonies in eastern Antarctica (Ainley and DeMaster, 1980). Annual survival is higher in larger penguins, and has been shown to be higher in species that begin breeding later in life and have a higher longevity (Croxall and Davis, 1999). As chinstrap and macaroni penguins are slightly smaller than adelie penguins, with nearly the same lifespan, the P/B of both groups was set slightly higher at 0.3y−1. Emperor penguins have higher annual adult survival rates of 0.9 to 0.95y−1 (Bried et al., 1999; Croxall and Davis, 1999). As these were for adult survival, the population P/B was assumed to be 0.15y−1. Gentoo penguins fall between emperor and adelie penguins in size, therefore their P/B was assumed to be 0.2y−1. Consumption was calculated using two general equations J.2 and J.3 to calculate the basal metabolic rate (BMR) and ﬁeld metabolic rate (FMR) for Sphenisciformes (Ellis and Gabrielsen, 2002) cited in Karpouzi (2005).

BMR = 1.775 · m0.768 (J.2)

FMR = 21.33 · m0.626 (J.3)

Where Basal and Field Metabolic Rates are given in kJ · d−1 and m=mass of the bird in grams. These metabolic rates were then applied to the number of breeding and non breeding days (table J.3) to give a yearly average of metabolic rates given in Karpouzi (2005). Yearly energy required was divided by average energy of all prey items weighted by proportion of diet to give a yearly weight. The calculated consumption rates were generally too high for the model and were decreased to balance the model.

389 Table J.3: Number of breeding and non-breeding days per year. Q/B values were calculated using equations J.2 and J.3. Data for number of breeding, non-breeding days per year, and weight from Karpouzi (2005).

Species Non- Breeding Calculated Model Weight P/B breeding days Q/B (y−1) Q/B (Kg) (y−1) days (y−1) Emperor Penguins 91 274 33.69 28.69 30 0.15 Gentoo Penguins 280 85 31.65 29 6 0.2∗ Chinstrap Penguins 257 108 38.95 34 4.5 0.3∗ Macaroni Penguin 246 119 35.12 25 4.5 0.3 Adelie Penguins 257 108 38.64 30 4.75 0.29 ∗ P/B values were increased for gentoo and chinstrap to account for immigration. 390 J.1. Model Parameters by Functional Group

Emperor Penguins

(Aptenodytes forsteri) Emperor penguins are the largest seabirds in the Antarctic, weighing up to 40kg (Kirkwood and Robertson, 1997), and have a circumpolar distribution. Adult female penguins must fast while laying their eggs, and then they have a short amount of time to replenish energy stores before returning to breeding sites to feed newly hatched chicks and then fast once more. Adult males take over responsibility of caring for the unhatched egg, and also must live off energy reserves during this time. Therefore availability of food resources is important to emperor penguins, as both male and female adults require sufficient reserves to survive the winter. Emperor penguin biomass was assumed to be the lowest of all penguin biomasses as they tend to be located on the continent, and was estimated to be roughly 10% of adelie penguin numbers (Ainley et al., 1994) or 0.0013t·km−2, but was increased to 0.005t·km−2 to balance the model. Cephalopods and fish are important contributors to the diets of emperor penguins. Cephalopod contributions can range from 3-99% of the diet, with fish ranging from 38-97%, depending on season and location around Antarc- tica (Klages, 1989; Kirkwood and Robertson, 1997; Cherel and Kooyman, 1998). P. antarcticum was the most prevalent fish in the diet for all locations. Amphipods were noted to increase frequency in the diet in the spring, with benthic prey being rare year round. Krill also fluctuated in the diet, from 70% of the winter diet in female penguins to 25% of the late summer diet (Green, 1986; Klages, 1989; Putz, 1995). The diet was set to 22% cephalopods, 1% other icefish, 3% Large notothenioids, 3% small notothenioids, 20% P. antarcticum, 0.1% arthropod crustaceans, 29.9% adult krill, and 21% sub-adult krill (Green, 1986; Klages, 1989; Putz, 1995; Kirkwood and Robertson, 1997; Cherel and Kooyman, 1998).

Gentoo Penguins

(Pygoscelis papua) Gentoo penguins are deep diving, inshore feeding penguins which require large amounts of food near their colonies in order to feed their young (Trivelpiece et al., 1987). During the breeding season gen-

391 J.1. Model Parameters by Functional Group too penguins do not fast, however their ability to make deeper dives than adelie and chinstrap penguins allows them to utilize prey species found in deeper water. Of the global population of 298,000 breeding pairs, 30% were found at South Georgia, and 15% were found within the pack ice region (Trivelpiece et al., 1987). While the Antarctic Peninsula is a smaller population compared to colonies on South Georgia, it was assumed 10% or 29,800 pairs were in the model area leading to a biomass of 0.0005t·km−2. However this had to be increased to 0.0065t·km−2 to balance the model. Although this biomass is relatively high, even compared to numbers at South Geor- gia, a higher biomass was necessary to account for the predation mortality primarily from killer whales and leopard seals. As gentoos have increased in abundance at a number of locations on the peninsula (PALMER station on Anvers Island, and Cierva point on Signey Island), a biomass accumulation term was included in the model to account for migration into the region (Quintana et al., 2000; Fraser, 2006). One population at Cierva point on the peninsula has increased an average of 5.7% per year from 1991-1996 (Quintana and Cirelli, 2000), while the population at PALMER station has increased nearly 50 fold from 1991 to 1996 (Fraser, 2006). While data from PALMER station is not believed to be representative of the entire model area, a biomass accumulation of 5.7% a year was incorporated into the model. The production was increased from 0.2 to 0.22y−1 to account for the biomass accumulation. The diet is dominated by ﬁsh (primarily N. rossii , N. neglecta, C. gunnari, with some myctophids), krill; contributing on average 50% of the diet, and cephalopods with a few amphipods (Volkman et al., 1980; Crox- all et al., 1988; Coria et al., 2000; Clausen and Putz, 2003; Lescroel et al., 2004; Clausen et al., 2005). The diet was set to 32% cephalopods, 1% other iceﬁsh, 4% large notothenioids, 4% small notothenioids, 1% deep demersals large, 3% deep demersals small, 1% C. gunnari, 1% P. antarcticum, 8% N. gibberifrons, 2% salps, 2% urochordata, 1% porifera, 2% hemichordata, 1.5% brachiopoda, 1.5% bryozoa, 3% cnidarians, 1% crustaceans, 1% worms, 23% adult krill, 2% sub-adult krill, and 5% macro-zooplankton.

392 J.1. Model Parameters by Functional Group

Chinstrap Penguins

(Pygoscelis antarctica) Chinstrap penguins are the second most abundant penguin species in the world, with the majority of the population in the Sco- tia Sea region (Knox, 1994; MacDonald et al., 2002). Range has expanded onto the Antarctic Peninsula with some colonies increasing 6-10% per year or even higher (Fraser et al., 1992). Estimates from the Weddell Sea range from 0.007 to 0.003t·km−2 for areas with ice and without (Van Franeker et al., 1997). The biomass was assumed to be similar to the Weddell Sea and was set to a mid-range value of 0.0053t·km−2. Trends from different islands in the model area identify increases in abundance from the 1950s to the 1980s with fluctuating populations up until the 2000s; of the three study sites, one population increased, one decreased, and one fluctuated from 1980-2000 (Croxall et al., 2002). However, long term data provided from PALMER station (figure J.1) was used in model fitting, as it was the only available data. A biomass accumulation of 10% per year was incorporated to account for chinstrap penguins moving from other areas onto the peninsula. To account for the biomass accumulation, the P/B ratio was increased from 0.3 to 0.33y−1. Chinstrap penguins are believed to feed exclusively on krill during the breeding season, adjusting the diving depth to coincide with the depth where krill are present (Volkman et al., 1980; Bengtson et al., 1993; Takahashi et al., 2003). Fish, cephalopods, and various benthic species have also been found in the diet, with fish increasing in frequency in areas where adelie and chinstrap penguins overlap in distribution. The increase of fish in the chinstrap diet is thought to be caused by increased competition for krill (Lynnes et al., 2004). The diet was set to 38% cephalopods, 1% other icefish, 3% large notothenioids, 1% small notothenioids, 1% deep demersals large, 3% deep demersals small, 1% C. gunnari, 1% P. antarcticum, 4% N. gibberifrons, 2% salps, 2% urochordata, 2% porifera, 2% hemichordata, 1.5% brachiopoda, 1.5% bryozoa, 3% cnidarians, 1% crustaceans, 2% worms, 23% adult krill, 2% sub-adult krill, and 5% macro-zooplankton (Volkman et al., 1980; Bengtson et al., 1993; Takahashi et al., 2003; Lynnes et al., 2004).

393 J.1. Model Parameters by Functional Group

Macaroni Penguin

(Eudyptes chrysolophus) Although macaroni penguins are believed to be the most abundant penguin species in the world (Green et al., 1998), they are considered less abundant than other penguins at the peninsula, contributing less than 1% of the total bird biomass in the Scotia arc-Weddell Sea region (Ainley et al., 1994). Based on total penguin abundance (Van Franeker et al., 1997), average weight (Davis et al., 1989), and distribution of individual penguin species (Whitehouse and Viet, 1994), 1% of total bird biomass, including flying birds would be 0.0008t·km−2, however this was too low for the model, and the biomass was increased to 0.0135t·km−2, or nearly 15% of the biomass of other bird species. Fish in the diet consists primarily of myctophids, icefish, and notothenioids, which ranges from a small contribution up to half of the diet depending on location (Croxall et al., 1988; Davis et al., 1989; Klages, 1989; Green et al., 1998). Krill is an important prey item during chick rearing which can contribute up to 95% of the total diet. Amphipods and mysidaceans were also present in the diet along with cephalopods (Klages, 1989). The yearly average diet was set to 11% cephalopods, 1.5% other icefish, 5% large notothenioids, 2% small notothenioids, 0.5% deep demersals small, 2% myctophids, 1% other pelagics, 2% C. gunnari, 1% P. antarcicum, 2% N. gibberifrons, 3% crustaceans, 35% adult krill, and 34% sub-adult krill (Croxall et al., 1988; Davis et al., 1989; Klages, 1989; Green et al., 1998).

Adelie Penguins

(Pygoscelis adeliae) Adelie penguins are the most abundant penguins over the entire peninsula with estimates ranging from 3.5 animals·km−2 for inshore areas to less than 1 animal·km−2 for oﬀshore areas (Whitehouse and Viet, 1994). Estimates of 625,800 penguins over three of the Shetland Islands indicate high densities at centralized locations (Trivelpiece et al., 1987). An average of 2.84 animals·km−2 yielded a biomass of 0.016t·km−2, however this was too low and considering estimates from the Shetland Islands, the biomass was increased to 0.034t·km−2 to balance the model. Densities in

394 J.1. Model Parameters by Functional Group the Weddell Sea reach 8 animals·km−2 on sea ice (Van Franeker et al., 1997), a higher density than noted at the peninsula even though there are believed to be more adelie penguins in the peninsula region. Fish, cephalopods and krill were the most important prey items to adelie penguins, with krill ranging up to 100% of the diet during breeding season (Volkman et al., 1980). Of the ﬁsh prey items P. antarcticum was the most prevalent. Amphipods have been noted as a minor contributor to the diet (Green and Johnstone, 1988; Kent et al., 1997; Kerry et al., 1997; Ainley et al., 2003; Efran and Pitcher, 2005). Diet composition was set to 2% cephalopods, 0.3% shallow demersals, 0.5% myctophids, 1% other pelagics, 4% P. antarcticum, 8% molluscs, 6% crustaceans, 66.2% adult krill, 10% juvenile krill, and 2% macro-zooplankton (Volkman et al., 1980; Green and Johnstone, 1988; Kent et al., 1997; Kerry et al., 1997; Ainley et al., 2003; Efran and Pitcher, 2005).

Flying birds

The functional group for ﬂying birds contains all species known to inhabit the Antarctic Peninsula either part time or full time based on a global database (Karpouzi, 2005). This includes the following species: southern giant fulmar or southern giant petrel (Macronectes gigcialoides), Antarctic petrel (Thalassoica antarctica), Snow Petrel (Pagodroma nivea), Domini- can Gull (Larus dominicanus), Grey headed Albatross (Diomedea chrysostoma), Light-mantled Sooty Albatross (Phoebetria palpebrata), Cape Pe- trel (Daption capense), Blue Petrel (Halobaena caerulea), Antarctic Prion (Pachyptila vittata), Kerguelen Petrel (Lugensa brevirostris), Diving Petrel (Pelecanoides urinatrix), Wandering Albatross (Diomedea exulans), Black- Browed Albartoss (Diomedea melanophrys), White chinned petrel (Pro- cellaria aequinoctialis), Sooty Shearwater (Puﬃnus griseus), Fairy Prion (Pachyptila turtur), Soft-Plumaged Petrel (Pterodroma mollis), Black-Bellied Storm Petrel (Fregetta tropica), Wilson’s Storm Petrel (Oceanites oceani- cus), American Sheathbill (Chionis alba), Brown Skua or subantarctic skua (Catharacta skua), South Polar Skua (Catharacta maccormicki), Antarc-

395 J.1. Model Parameters by Functional Group tic Tern (Sterna vitatta), Arctic Tern (Sterna paradisaea), Southern Gi- ant Petrel (Macronectes giganteus), Blue-eyed Cormorant or blue eyed shag (Phalacrocorax atriceps), Southern Black-backed Gull (Larus dominicanus), Yellow-billed Sheathbill (Chionis alba), Grey-headed Albatross (Diomedea chrysostoma). Van Franeker et al. (1997) provided a biomass for 15 species in the Wed- dell Sea region of 0.087tcot km−2, while Whitehouse and Viet (1994) identified the biomass of 21 species at the Antarctic Peninsula to be 0.199tcot km−2. The latter value was used for the model. Consumption was calculated using an average daily food intake (DFI) value provided by Karpouzi (2005) and comparing it to the energetic value of the prey items in the diet. This provided a Q/B value of 14.88 y−1. The P/B ratio was estimated by the model using an EE of 0.95. The diet of flying birds is highly varied among species including predation on other birds and penguins. Diet for this group was set to: 0.1% adelie penguins, 2.1% flying birds, 21.6% cephalopods, 1% other icefish, 0.1% large notothenioids, 2% small notothenioids, 2% myctophids, 1.6% other pelagics, 1.8% P. antarcticum, 0.1% N. gibberifrons, 3.2% mollusca, 5% salps, 0.5% cnidarians, 7% crustaceans, 23.4% adult krill, 23.5% sub-adult krill, and 5% copepods (Pakhomov et al., 2002; Karpouzi, 2005).

Cephalopods

Species of this group include all known cephalopods which have been found in the model area: Alluroteuthis antarcticus, Bathyteuthis abyssicola, Gali- teuthis glacialis, Mesonychoteuthis hamiltoni, Moroteuthis knipovitchi, and Psychroteuthis glacialis (Xavier et al., 1999), and based on stomach contents of predators (Daneri et al., 2000). The biomass of all cephalopods in the area set to 2.49tcot km−2 based on estimates from Jackson et al. (2002). The P/B and Q/B were based on values used for cephalopods in the Ker- guelen Islands model (Jarre-Teichmann et al., 1997; Pruvost et al., 2005). P/B was set to 0.95 year-1 based on values of 0.6 and 1y−1 for small and large cephalopods, respectively. Q/B was initially set to 2.5y−11 considering values of 2 and 3y−1 for large and small cephalopods; however this value was

396 J.1. Model Parameters by Functional Group too high and was lowered to 2y−1 to balance the model. The diet of cephalopods was set to 1% cephalopods, 0.1% other iceﬁsh, 0.05% toothﬁsh, 0.1% large notothenioids, 0.5% small notothenioids, 1% myctophids, 1% other pelagics, 7.5% P. antarcticum, 1% mollusca, 4.25% salps, 0.4% urochordata, 0.5% cnidarians, 5% crustaceans, 3.25% arthropod other, 4.9% worms, 11.8% adult krill, 33% juvenile krill, 13.1% macro- zooplankton, and 8% micro-zooplankton (Hureau, 1994; Lu and Williams, 1994; Kozlov, 1995; Rodhouse and Nigmatullin, 1996).

Fish

Fish groupings were based on familial characteristics and feeding preferences. Those species known to be important prey items to a variety of predators were given their own functional grouping. Factors taken into account for groupings were size, depth, family, feeding strategy, and habitat preference (Daniels and Lipps, 1989; Knox, 1994; Barrera-Oro et al., 2000; Kock et al., 2000). Biomass for all fish groups was estimated from surveys, relative abundance data, presence-absence data, known ranges for each species, and then broken down by species to give each group biomass. In general species were reported as a percentage of total catch or a biomass was given for individual species (Kock, 1992; Knox, 1994; Frolkina et al., 1998; Kock, 1998; Arana and Vega, 1999; Jones et al., 2000; Kock et al., 2000, 2004; Kock and Jones, 2005; Froese and Pauley, 2008). It is likely that biomass estimates from these surveys will underestimate the fish biomass, and in some cases biomass was increased to balance the model. Total mortality was set to the sum of fishing mortality and natural mortality. Fishing mortality occurs to all functional groups except the demersal fish, however the fishing mortality caused within the first year was set to be negligible (see fishery section). Therefore, natural mortality (M) was assumed to equal the Production/Biomass ratio, and was calculated using 2 methods. The first equation J.4 (Pauly, 1980; Froese and Pauley, 2008) and

397 J.1. Model Parameters by Functional Group equation J.5 (Jensen, 1997) with the results in table J.4.

M = 100.566−0.718·logL∞+0.02·T (J.4)

M = 1.5k (J.5)

where L∞ is the maximum length a fish would grow to in a population and T represents temperature in degrees Celsius, which was set to 0.5◦C as a yearly average (Dierssen et al., 2002). Max length values were taken from fishbase and published literature (Daniels, 1982; FAO, 1985a,b; Kock, 1992; Frolkina et al., 1998; Arana and Vega, 1999; Kock et al., 2000, 2004; Kock and Jones, 2005; Froese and Pauley, 2008). Although equation J.4 is based on 175 fish stocks, it underestimates the mortality for polar species (Pauly, 1980), so a second equation (eq. J.5) for mortality was used in comparison; where k is the growth coefficient. If mortality rates could be calculated for all or most species in the functional group then the average mortality rate was taken. If the value was known for only one species, than that value was used for the functional group. Consumption rates were calculated using equation J.6 (Palomares and Pauly, 1998);

Q ′ log = 7.964 − 0.204 · logW∞ − 1.965T + 0.532h + 0.398d (J.6) B

Where W∞ is the weight a fish would reach if it grew to L∞, T is the mean environmental temperature (1000 / (C + 273.15)) with C representing temperature in degrees Celsius, A is the aspect ratio of the caudal fin (with the default value= 1.32), h and d represent variables for feeding types; h=1 if the fish is a herbivore, h=0 if it consumes other food types, d=1 if the fish is a detritivore, d=0 if the fish consumes other food types. Temperature was set to 0.5◦C based on winter and summer temperatures (Dierssen et al., 2002). Pakhomov (unpublished data) indicate daily consumption rates of demersal fish ranging from 0.5-4% of body weight, leading to annual Q/B ratios from 1.82-14.6y−1. This indicates equations from Palomares and Pauly (1998) may be underestimating consumption of polar species.

398 Table J.4: Calculated mortality and consumption values for ﬁsh groups. Biomass (B) is presented from surveys in t·km−2. Mortality (M), production to biomass ration (P/B) and consumption to biomass (Q/B) are presented as a yearly rate (y−1)

Group B Species K Reference M1‡ M† Model Q/B∗ Model P/B Q/B 20 Other Iceﬁsh 0.337 Family value 0.273 Froese and Pauley (2008) 0.409 0.32 0.38 1.57 1.57 P. georgianus 0.32 Froese and Pauley (2008) 0.48 21 Toothﬁsh 0.047 D elegiodes 0.102 Horn (2002) 0.152 0.14 0.165 0.7 0.77 D. mawsoni 0.099 Horn (2002) 0.148 22 Lg. Nototheniidae 0.59 Family value 0.133 Froese and Pauley (2008) 0.19 0.19 0.37 2.76 1.95 N. coriiceps 0.098 Coggan (1997) 0.147 23 Sm. Nototheniidae 0.341 Family value 0.364 Froese and Pauley (2008) 0.546 0.43 0.65 2.53 2.2 24 Shallow Demersals 0.031 H. antarcticus 0.14 Daniels (1983) 0.21 0.37 0.75 4.65 4.125 H. antarcticus 0.25 Daniels (1983) 0.375 25 Deep Demersals Lg. 0.042 P. brachy- 0.31 Froese and Pauley (2008) 0.465 0.18 0.29 2 2.18 cephalum O. amberensi 0.31 Froese and Pauley (2008) 0.465 26 Deep Demersals Sm. 0.08 p. brevipes Froese and Pauley (2008) 0.4 0.65 2.7 2.7 27 Myctophids 0.185 Family value 0.43 Froese and Pauley (2008) 0.64 0.53 1.35 3.4 3.73 28 Other Pelagics 0.49 A. pharao 0.5 Froese and Pauley (2008) 0.75 0.22 0.55 1.83 2.02 B. antarcticus 0.14 Froese and Pauley (2008) 0.21 29 C. gunnari 0.29 C. gunnari 0.141 Froese and Pauley (2008) 0.212 0.22 0.48 2.4 1.8 30 P. antarcticum 1.25 P. antarcticum 0.093 Froese and Pauley (2008) 0.14 0.19 1.1 1.1 3.55 31 N. gibberifrons 0.81 N. gibberifrons 0.104 Froese and Pauley (2008) 0.156 0.11 0.41 1.4 1.55 †Natural mortality calculated using eq. J.4 (Pauly, 1980) ‡Natural mortality calculated using eq. J.5 (Jensen, 1997) ∗Consumption calculated using eq. J.6 (Palomares and Pauly, 1998) 399 J.1. Model Parameters by Functional Group

Other Iceﬁsh

This group represents all icefish species with the exception of C. gunnari which is an important prey item for many species, and thus was given its own functional group. All other icefish in the area consist of; Chaeno- cephalus aceratus, Chaenodraco wilsoni, Chionodraco rastrospinosus, Cry- odraco antarcticus, Neopagetopsis ionah, Pagetopsis macropterus, Chiono- bathyscus dewitti and Pseudochaenichthys georgianus. Diet was set to 1.5% cephalopods, 0.5% other icefish, 0.05% toothfish, 3.05% large notothenioids, 3.5% small notothenioids, 0.4% deep demersals large, 1.5% deep demersals small, 4% C. gunnari, 5% P. antarcticum, 18% N. gibbifrons, 2% mollusca, 3% salps, 2.5% cnidarians, 3% crustaceans, 0.5% arthropods other, 2% worms, 18.5% adult krill, 19% sub-adult krill, 7% macro-zooplankton, 3% micro-zooplankton, and 2% copepods (Pakhomov et al., 2002; Flores et al., 2004; Kock et al., 2004).

Toothﬁsh

The toothﬁsh group included two species: Dissostichus eleginoides and Dis- sostichus mawsoni. P/B biomass was increased slightly beyond the calculated values to 0.165y−1 in order to balance the model The diet was set to: 17.4% cephalopods, 20% other iceﬁsh, 6% large notothenioids, 15% small notothenioids, 0.5% deep demersals large, 1% deep demersals small, 2% myctophids, 1% other pelagics, 5% C. gunnari, 4% P. antarcticum, 8% N. gibbifrons, 1.1% salps, 0.5% cnidarians, 8.5% crustaceans, 1% other arthropods, 1% worms, 4% adult krill, and 4% sub-adult krill (Garcia de la Rosa et al., 1997; Arana and Vega, 1999).

Large Notothenioids

Large Nototheniidae were classiﬁed as ﬁsh in the family Notothenidea with an average length over 30 cm. This included Notothenia coriiceps, No- tothenia (Notothenia) neglecta, Notothenia rossii, Pagothenia (Tremato- mus) hansoni and Notothenia squamifrons. The P/B ratio was increased

400 J.1. Model Parameters by Functional Group to 0.37y−1 to balance the model, and the diet was set to: 0.5% large notothenioids, 1.5% small notothenioids, 0.1% shallow demersals, 0.1% deep demersals large, 0.25% deep demersals small, 2% myctophids, 2% other pelagic, 0.5% C. gunnari, 2% P. antarcticum, 0.5% N. gibberifrons, 4% mollusca, 3% salps, 0.25% cnidarians, 28.4% crustaceans, 2% other arthropods, 7% worms, 16.8% adult krill, 16% juvenile, 0.1% larval krill and krill embryos together, 3% Macro-zooplankton, 5% Ice algae, and 5% other phytoplankton (Casaux et al., 1990; Kozlov, 1995; Pakhomov et al., 2002).

Small Notothenioids

Small notothenioids were classiﬁed as ﬁsh from the family Notothenoidea with an average length less than 30 cm. This included Cryothenia peninsu- lae, Notothenia (Lepidonotothen) larseni, Notothenia (Lepidonotothen) nud- ifrons, Trematomis loennbergi, Pagothenia (Trematomus) bernacchii, Tremato- mus newnesi, Trematomus scotti, Trematomus eulepidotus, and Trematonius centronotus. The diet for these species was set to: 11% mollusca, 2% salps, 1% urochordates, 1% cnidarians, 35% crustaceans, 0.1% other arthropods, 19% worms, 0.2% Echinoidea, 0.2% Crinoidea, 0.2% Ophiuoidea, 0.2% As- teroidea, 1.1% Holothuroidea, 10% Adult krill, 10% juvenile krill, 0.1% larval krill, 3.9% Macro-zooplankton, 2% Micro-zooplankton, and 3% copepods (Casaux et al., 1990; Vacchi et al., 1994; Pakhomov et al., 2002).

Shallow Demersals

Shallow demersals were classiﬁed as demersal ﬁsh typically found in depth ranges of 0-200m. This included Artedidraco skottsbergi, Harpagifer antarcticus, and Harpagifer bispinis. The P/B ratio was increased to 0.75y−1 to balance the model, and diet was set to: 7.5% Mollusks, 2% salps, 75% crustaceans, 2% other arthropods, 4.5% worms, 7% adult krill, and 2% sub-adult krill (Duarte and Moreno, 1981; Casaux, 1998; Pakhomov et al., 2002).

401 J.1. Model Parameters by Functional Group

Large Deep Demersals

This group was characterized by an average depth of 200 m or deeper, and an average size of 30 cm or larger. This included Parachaenichthys char- coti, Gymnodraco acuticeps, Mancopsetta maculata, Muraenolepis microps, Pachycara brachycephalum, Paradiplospinus antarcticus, Ophthalmolycus amberensi, Bathyraja eatonii, Bathyraja maccaini, and Bathyraja sp2. Diet was set to: 5% cephalopods, 3.5% other iceﬁsh, 0.5% toothﬁsh, 4% large notothenioids, 4% small notothenioids, 2% shallow demersals, 7% deep demersals small, 2% C. gunnari, 7% P. antarcticum, 2% N. gibbifrons, 15% mollusca, 1% salps, 2% urochordates, 0.5% hemichordates, 2% cnidarians, 7% crustaceans, 0.5% other arthropods, 4.5% worms, 12% adult krill, 9% juvenile krill, 1% larval krill and krill embryo, 4% macro-zooplankton, 2% micro-zooplankton, and 2.5% other phytoplankton.

Small Deep Demersals

Small deep demersals were categorized by having an average depth of 200m or greater, and an average size of 30 cm or less. This included Pogonophryne marmorata, Prionodraco evansii, Psilodraco breviceps, and Paraliparis antarcticus. The diet for this group was set to: 4% cephalopods, 5% other ice- ﬁsh, 7% small notothenioids, 0.5% shallow demersals, 1% deep demersals small, 4% myctophids, 4% other pelagic, 8% P. antarctucim, 15% mollusca, 2% salps, 0.5% urochordata, 0.5% bryozoa, 0.1% cnidarians, 20.5% crustaceans, 0.5% other arthropods, 8% worms, 8.4% adult krill, 4.9% juvenile krill, 0.5% larval krill, 0.1% krill embryo, 5% macro-zooplankton, and 0.5% micro-zooplankton.

Myctophids

Fish belonging to the family Myctophidae were included in this group, which carry considerable vertical migration to utilize food and resources in the epipelagic zone. For this region this includes: Electrona antarctica, Gymno- scopelus braueri, Gymnoscopelus nicholsi, Gymnoscopelus opisthopterus, and Protomyctophum bolini. Both P/B and Q/B ratios were increased to 1.35y−1

402 J.1. Model Parameters by Functional Group and 3.73y−1 respectively to balance the model. The diet was set to: 25% mollusca, 2% salps, 23% crustaceans, 1% worms, 15% adult krill, 5% juvenile krill, 4% macro-zooplankton, and 25% copepods (Hureau, 1994; Kozlov, 1995; Greely et al., 1999; Pakhomov et al., 2002; Shreeve et al., 2005).

Other Pelagics

Other pelagic included all other species inhabiting the pelagic zone not in the family Myctophidae: Anotopterus pharaoh, Bathylagus antarcticus, Lam- pris immaculatus, Paradiplospinus gracilis, and Paradiplospinus antarcticus. The diet was set to: 25% cephalopods, 1% other iceﬁsh, 1.5% small notothenioids, 0.1% deep demersals large, 0.1% deep demersals small, 3% myctophids, 2% other pelagics, 10% P. antarcticum, 4% mollusks, 5% salps, 0.5% brachiopods, 0.5% bryozoans, 1.5% cnidarians, 5% crustaceans, 2% worms, 16% adult krill, 16.9% juvenile krill, 4% macro-zooplankton, and 1.9% micro-zooplankton (Jackson et al., 2000; Pakhomov et al., 2002).

Champsocephalus gunnari

For C. gunnari, the P/B ratio was increased to 0.48y−1, and the Q/B ratio was lowered to 1.8y−1 to balance the model. The diet was set to: 1% myctophids, 3% salps, 1% arthropod crustaceans, 1.5% worms, 47% adult krill, 44.5% juvenile krill, 1% macro-zooplankton, 1% micro-zooplankton (Kock and Everson, 2003; Flores et al., 2004).

Pleuragramma antarcticum

The P/B and Q/B ratios were increased to 1.1y−1 and 3.55y−1 respectively to balance the model. The diet for this group was set to: 0.1% other iceﬁsh, 0.1% small notothenioids, 0.1% deep demersals small, 1% other pelagic, 0.5% P. antarcticum, 1% N. gibberifrons, 13.3% mollusks, 1% salps, 0.1% cnidarians, 10% crustaceans, 1% other arthropods, 3% worms, 4% adult krill, 35% juvenile krill, 8% macro-zooplankton, 3.8% micro-zooplankton, and 18% copepods (Eastman, 1985; Hubold, 1985).

403 J.1. Model Parameters by Functional Group

Notothenia gibberifrons

The P/B ratio for N. gibbifrons was increased to 0.41y−1 to balance the model while the diet was set to: 2% mollusks, 1% salps, 1% urochordates, 1% cnidarians, 38% crustaceans, 1% other arthropods, 12% worms, 1% holothuroideans, 12% adult krill, 14% juvenile krill, 0.1% larval krill, 2.9% macro-zooplankton, 1% micro-zooplankton, 1% cryptophytes, 1% copepods, 1% diatoms, 5% ice algae, and 5% other phytoplankton (Casaux et al., 1990, 2003).

Invertebrates

Grouping for invertebrates were based on previous models of Antarctic peninsula and Weddell Sea regions (Jarre-Teichmann et al., 1997; Efran and Pitcher, 2005) taking into account invertebrate groups important to the diets of top predators. Species with low biomass or those not signiﬁcantly contributing to the diet of higher level predators were generally combined to make one larger species group. Likewise, species which were quite important to higher predators were split into one or more groups.

Table J.5: Benthic habitat by depth range for the Antarctic Peninsula

Depth Percentage of total Habitat <10m 1.66 11-50m 3.94 51-100m 3.89 101-200m 6.59 201-1000m 33.99 >1000m 49.94

Jazdzewski et al. (1986) provided benthic surveys from 18 stations ranging from 15-250 meters in depth at King George Island in the South Shet- lands for the 1980s. Saiz-Salinas et al. (1998) sampled 73 stations ranging from 32-421 meters between 1994-1995 near Livingston Island in the South Shetlands. Piepenburg et al. (2002) re-sampled King George Island in 1998 taking transects 130-2000 meters. These three surveys provided biomass estimates for each of the functional groups in the model, at various depths.

404 J.1. Model Parameters by Functional Group

The ﬁnal biomass was based on the average biomass for each depth range compared to percentage of habitat for each depth (table J.5), as provided by the GIS basemap function in Ecopath version 5 (Christensen et al., 2005). Invertebrate groups where published production values could not be found, were calculated using equation J.7 (Brey, 1999) where B is the biomass (g DM·m−2), M is maximum individual body mass (g DM), T is the surface temperature of the water (◦C), and D is the depth of water (in meters). Temperature was set to 0.5◦C, and depth was taken as the average depth the functional group was found in surveys. Individual body mass was taken from Saiz-Salinas et al. (1998) and converted to dry mass (DM) using values in Brey (2004, 2009). log(P ) = 0.240+0.960·log(B)−0.210·log(M)+0.030·T −0.160·log(D +1) (J.7) Consumption rates were based on published literature as shown (table J.6), and diet information was provided on a per species basis. However, diet information was generally provided for summer months, when most research is conducted in the Antarctic. It was formerly believed that feeding ceases in the winter months, however recent studies (Barnes and Clarke, 1995; Peck et al., 2005) identify feeding throughout most of the winter. It has been suggested that ice scour, which directly damages benthic communities, may also help re-suspend particles in the sediment making them available for suspension feeders (Orejas et al., 2000). Antarctic brachiopods which take advantage of the abundant summer food supply, however in the winter they rely on re-suspended benthic material (Peck et al., 2005). It is likely other benthic species also rely on this strategy for feeding during the winter months. Therefore, annual diets have been adjusted to incorporate re-suspension of detritus as a food source.

Molluscs

Surveys revealed the biomass and abundance of this group was dominated by bivalves. Other taxonomic groups included Gastropods, namely Opis- tobranchs (sea slugs) and Prosobranchs (snails), and in smaller numbers

405 J.1. Model Parameters by Functional Group

Scaphopods (tusk shells). Solenogastres (Aplacophors or shell-less mollusks) were also present, but not a substantial part of this functional group. While the majority of bivalves were assumed to be ﬁlter feeders, other species of molluscs have been reported to consume diﬀerent types of worms (Jarre- Teichmann et al., 1997). The diet for this group was heavily weighted to account for large amounts of bivalves and was set to: 6% worms, 1% macro- zooplankton, 2% micro-zooplankton, 2% cryptophytes, 1% copepods, 5% diatoms, 5% ice algae, 5% other phytoplankton, and 73% detritus.

Urochordata

This group was primarily comprised of ascidians or sea squirts, and includes all urochordates except salps. As ﬁlter feeders (Jarre-Teichmann et al., 1997) the diet was set to 10% micro-zooplankton, 15% cryptophytes, 3% copepods, 15% diatoms, 15% ice algae, 30% other phytoplankton, and 12% detritus.

Porifera

Based on surveys sponges are quite abundant at the peninsula, and they have been shown to be important to the diets of various echinoderms. As ﬁlter feeders, the diet has been noted to consist primarily of detritus (Jarre- Teichmann et al., 1997). The diet was set to 2% cryptophytes, 2% diatoms, 2% ice algae, 2% other phytoplankton, and 92% detritus.

Hemichordata

Acorn worms (class Enteropneusta) were the only representatives found in surveys. In the Weddell Sea they are assumed to be complete detritivores (Jarre-Teichmann et al., 1997), so the diet was set to 100% detritus for the peninsula as well.

Brachiopoda

Brachiopods, or lampshells were not shown to be a signiﬁcant contribution to invertebrate biomass through surveys. They have the ability to switch

406 J.1. Model Parameters by Functional Group from pelagic feeding, taking advantage of the summer phytoplankton, to benthic food sources such as the re-suspended particles (Peck et al., 2005). The diet was set to, 10% micro-zooplankton, 5% cryptophytes, 5% copepods, 5% diatoms, 5% ice algae, 20% other phytoplankton, and 50% detritus.

Bryozoa

Bryozoans were found in most of the survey samples taken from the region. As ﬁlter feeders, they generally consume smaller particles (Barnes and Clarke, 1995). The diet was set to 5% micro-zooplankton, 15% cryptophytes, 5% copepods, 15% diatoms, 15% ice algae, 15% other phytoplankton, and 30% detritus.

Cnidarians

The cnidarian group is primarily comprised of sea anemones (anthozoans), sea fans (gorgonians), and hydroids (hydrozoans), but includes all pelagic and sessile stages of reproduction. Hydroids and anthozoans have been shown to consume a variety of foods such as diatoms, invertebrate larvae and eggs, copepods, nematodes, salps, and detritus (Orejas et al., 2001). The diet was set to 10% salps, 5% macro-zooplankton, 20% micro-zooplankton, 10% cryptophytes, 5% copepods, 5% diatoms, 5% ice algae, 10% other phytoplankton, 30% detritus.

Crusteceans

Arthropods were split into three main groups: crustaceans, other arthropods, and krill. The crustacean group represents all crustaceans except krill and includes the following taxa based on survey samples; loricata, ostracoda, leptostraca, cumacea, tanaidacea, isopoda , and amphipoda. Am- phipods and isopods had the highest contribution to biomass of this group. In the Arctic amphipods feed primarily on ice algae as juveniles, moving on to calanoid copepods as they mature (Scott et al., 2001). The diet for crustacenas was set to; 1% porifera, 0.5% bryozoa, 0.5% cnidarians, 1% crustacean, 0.5% arthropod other, 5.5% worms, 3% holothuroidea, 10% macro-

407 J.1. Model Parameters by Functional Group zooplankton, 9% copepods, 10% ice algae, 5% other phytoplankton, and 54% detritus (Jarre-Teichmann et al., 1997; Scott et al., 2001; De Broyer et al., 2003).

Other Arthropods

The remaining arthropods found in sample surveys were pycnogonidia (sea spiders) and acari (arachnids: ticks and mites). The biomass of these remaining arthropods was lower than the crustaceans, and they were separated primarily due to the dietary importance of crustaceans to higher level organisms. The diet for the group was set based on pycnogonida diet information at: 8% mollusks, 1% salps, 5% urochordata, 1% porifera, 1% bryozoa, 1% cnidarians, 5% crustaceans, 1.5% other arthropods, 23% worms, 0.1% echinoidea, 0.1% crinoidea, 0.1% ophiuroidea, 3.1% asteroidea, 12% holothuroidea, 0.5% juvenile krill larvae, 0.2% krill embryo, 5.5% macro- zooplankton, 4% micro-zooplankton, 2.5% copepods, and 25.4% detritus (Child, 1998).

Worms

The worm functional group contains all worms except the hemichordates. Surveys show a variety of ﬂatworms (Turbellaria), ribbon worms (Nemer- tini), peanut worms (Sipuncula), roundworms (Nematoda), ringed worms (Polychaeta, Oligochaeta, and Hirudinea), and penis worms (Priapulida). As these groups are a combination of ﬁlter feeders and detritivores the diet was set to 3% mollusks, 0.6% urochordata, 2.5% porifera, 0.1% bryozoa, 0.2% cnidarians, 0.2% crustaceans, 0.5% other arthropod, 3.9% worms, 1% echinoidea, 0.01% crinoidea, 2.5% ophiuroidea, 0.5% asteroidea, 1.7% holothuroidea, 15% macro-zooplankton, 4.5% micro-zooplankton, 3% diatoms, and 60.5% detritus (FAO, 1985a,b; Brueggman, 1998; Pakhomov et al., 2002).

408 J.1. Model Parameters by Functional Group

Echinoderms

Echinoderms were split into family groupings, as they are one of the largest phyla in the study in terms of biomass, and it is believed they are one of the most important groups of animals to transfer energy within the benthos (McClintock et al., 2005).

Echinoidea

Jacob et al. (2003) show the typical food of sea urchins to be sponges and hydroids (cnidarians) with bryozoans and diatoms also contributing to the standard diet. Other studies indicate a more diverse diet including ploy- chaetes, tunicates, diatoms, and algal matter (McClintock, 1994). The diet was set to 1% mollusks, 0.5% urochordata, 5% porifera, 0.05% hemichordata, 0.2% brachiopoda, 0.8% bryozoa, 1% cnidarians, 5% crustaceans, 2% other arthropods, 17.2% worms, 0.1% crinoidea, 1% ophiuroidea, 1% asteroidea, 4% holothuroidea, 0.1% krill embryo, 8% macro-zooplankton, 3% micro-zooplankton, 8% copepods, 2% diatoms, 2% ice algae, 5% other phytoplankton, and 33.1% detritus (Jacob et al., 2003).

Crinoidea

Crinoids (sea feathers) are the least abundant of all echinoderms, and are known to be ﬁlter feeders. The diet was set to 12.5% bryozoa, 4% arthropod crustaceans, 12.5% worms, 2% macro-zooplankton, 2% micro-zooplankton, 1% copepods, and 66% detritus (McClintock, 1994; Jarre-Teichmann et al., 1997).

Ophiuroidea

According to McClintock (1994) brittle stars consume a variety of food such as zooplankton, other brittle stars, detritus, polychaetes, diatoms, gastropods, and copepods. Other studies (Dearborn et al., 1996) show the top ﬁve prey groups to be sponges, ophiuroids, bivalves, polychaetes, and crustaceans. The diet for ophiuroids was set to 7% mollusks, 3% porifera, 0.3%

409 J.1. Model Parameters by Functional Group bryozoa, 0.5% cnidarians, 2.5% crustaceans, 10% worms, 5% ophiuroidea, 3.2% macro-zooplankton, 5.9% micro-zooplankton, 3.2% cryptophytes, 1% copepods, 2% diatoms, 2% ice algae, 2% other phytoplankton, and 52.4% detritus.

Asteroidea

The diet of asteroids has been shown to be quite diverse, including detritus, sponges, necrotic tissue, algae, zooplankton, fecal matter, and predation on other invertebrates (McClintock, 1994; Jarre-Teichmann et al., 1997). The diet for this group was set to; 1% mollusks, 1% salps, 1% urochordata, 2% porifera, 1% crustaceans, 5% worms, 5% ophiuroidea, 5% macro-zooplankton, 5% micro-zooplankton, 2% copepods, 2% diatoms, 2% ice algae, 2% other phytoplankton, 66% detritus.

Holothuroidea

Antarctic holothuroideans (sea cucumbers) are known to be suspension feeders (McClintock, 1994; Jarre-Teichmann et al., 1997), therefore the diet was set to 1% diatoms, 1% other phytoplankton, 98% detritus.

410 Table J.6: Published, calculated, and model mortality (P/B) and consumption (Q/B) rates for invertebrate groups

Model Group Model Calc. Group Source Model Pub Source P/B P/B Q/B Q/B 32 Mollusca 0.639 0.309 Mollusca Brey and Gerdes (1998) 2.556 Estimated by model 0.778 Bivalve Brey and Clarke (1993) 0.432 Bivalve Brey and Clarke (1993) 0.497 Gastropod Brey and Clarke (1993) 0.305 Benthic Mol- Jarre-Teichmann et al. lusca (1997) 33 Salps 10 33 Estimated by model 34 Urochordata 0.234 0.23 Tunicata Brey and Gerdes (1998) 1 1 Jarre-Teichmann et al. (1997) 0.1 Tunicata Jarre-Teichmann et al. (1997) 0.234 combined‡ Brey (2001) 35 Porifera 0.159 0.159 Porifera Brey and Gerdes (1998) 0.795 0.6 Efran and Pitcher (2005) 0.03 Porifera Jarre-Teichmann et al. (1997) 0.116 combined‡ Brey (2001) 36 Hemichordata 0.375 0.155 Hemichordata Brey and Gerdes (1998) 2 2 Jarre-Teichmann et al. (1997) 0.3 Hemichordata Jarre-Teichmann et al. (1997) 411 Continued on Next Page Table J.6 Continued Model Group Model Calc. Group Source Model Pub Source P/B P/B Q/B Q/B 37 Brachiopoda 0.898 0.1 Lophophora and Jarre-Teichmann et al. 4.5 1 Jarre-Teichmann et al. Cnidarians (1997) (1997) 0.815 combined‡ Brey (2001) 38 Bryozoa 0.475 0.1 Lophophora and Jarre-Teichmann et al. 1.75 1 Jarre-Teichmann et al. Cnidarians (1997) (1997) 0.227 combined‡ Brey (2001) 39 Cnidarians 0.25 0.186 Cnidarians Brey and Gerdes (1998) 0.1 Lophophora and Jarre-Teichmann et al. 1 1 Jarre-Teichmann et al. Cnidarians (1997) (1997) 40 Crusteceans 1.05 0.616 Arthropoda Brey and Gerdes (1998) 4.2 Estimated by model 0.794 Isopoda Brey and Clarke (1993) 0.397 Decapoda Brey and Clarke (1993) 0.7 benthic Crus- Jarre-Teichmann et al. 3.5 Jarre-Teichmann et al. tacea (1997) (1997) 41 Arthropod 0.616 0.616 Arthropoda Brey and Gerdes (1998) 3.326 Other benthic Crus- 3.5 Efran and Pitcher (2005) tacea and Chelicerata 42 Worms 0.7 0.319 Annelida Brey and Gerdes (1998) 3.2

412 0.168 Scolecida Brey and Gerdes (1998) Continued on Next Page Table J.6 Continued Model Group Model Calc. Group Source Model Pub Source P/B P/B Q/B Q/B 0.6 Polychaeta and Jarre-Teichmann et al. 4 Efran and Pitcher (2005) other worms (1997) all worms 4 Jarre-Teichmann et al. (1997) 43 Echinoidea 0.116 0.164 all echinoderms Brey and Gerdes (1998) 0.464 Estimated by model 0.116 Echinoidea Brey and Clarke (1993) 44 Crinoidea 0.125 0.164 all echinoderms Brey and Gerdes (1998) 0.1 Crinoidea Jarre-Teichmann et al. 0.8 1 Jarre-Teichmann et al. (1997) (1997) 45 Ophiuroidea 0.45 0.164 all echinoderms Brey and Gerdes (1998) 1.8 Estimated by model 0.566 Ophiuroidea Brey and Clarke (1993) 0.173 Ophiuroidea Jarre-Teichmann et al. (1997) 46 Asteroidea 0.231 0.164 All echinoderms Brey and Gerdes (1998) 0.924 Estimated by model 0.221 Asteroidea Brey and Clarke (1993) 0.164 Asteroidea Brey and Clarke (1993) 0.376 Asteroidea Brey and Clarke (1993) 47 Holothuroidea 0.315 0.164 all echinoderms Brey and Gerdes (1998) 0.1 Holothuroidea Jarre-Teichmann et al. 1.1 1.1 Jarre-Teichmann et al. (1997) (1997)

413 Continued on Next Page Table J.6 Continued Model Group Model Calc. Group Source Model Pub Source P/B P/B Q/B Q/B 0.315 combined‡ Brey (2001) †Where P/B values were calculated for various species within the functional group, average value is presented. ‡P/B was calculated using eq. J.7 (Brey, 2001) with the average value of all species presented. 414 J.1. Model Parameters by Functional Group

Zooplankton

Zooplankton surveys from the Antarctic peninsula and surrounding areas indicate the zooplankton biomass is dominated by krill (Euphausia superba) and copepods. Surveys indicating biomass divided the catches into taxonomic groupings generally based on biomass. For the model these survey results were used to delineate proportions of the total zooplankton biomass into the speciﬁc functional groups. Salps, krill, and copepods are separated from the rest of the zooplankton due to increased understanding of their roles within the ecosystem, and their importance to the food web. Calbert et al. (2005) estimated macro-zooplankton biomass ranging from 17-542 mgC·m−2 while the meso-zooplankton ranged from55-1741 mgC·m−2 for samples from the Gerlache Strait, Bransﬁeld Strait, and Bellinghausen Sea for 2002. The meso-zooplankton samples included krill, copepods, and salps so the biomass would be considerably lower when these groups were removed. Estimates from other areas of the Scotia Sea range up to 6150 mgC·m−2 (roughly 51 g·m−221 from a sample from South Georgia in 1994 sampling primarily meso-zooplankton. While the biomass at South Georgia is high, the Antarctic peninsula is considered a source population for krill, and potentially transports other zooplankton species (Brierley et al., 1999) indicating the total zooplankton biomass could be at least as high as South Georgia.

Salps

The salps group refers speciﬁcally to the tunicate Salpa thompsoni. Salps graze smaller phytoplankton such as cryptophytes (which are associated with warmer water temperatures and lower salinities), being able to reduce the amount of carbon available to predators by 70% (Moline et al., 2004), thus they were believed to be a trophic dead end in the food web. However research into their ecology indicates they are consumed by some ﬁsh and invertebrates (Dubischar et al., 2006). In warmer years salps tend to dominate

21Using the conversion 1gC=8.3 wet weight for general zooplankton conversion taken from Cushing et al. (1958) as cited in Cauﬀope and Heymans (2005)

415 J.1. Model Parameters by Functional Group the zooplankton biomass, whereas in cooler years diatoms are more available which increase the transfer of carbon to krill and then further up the food chain. Salps have been shown to remove a majority of primary production later in the summer (march) which may contribute to poor krill larvae biomass as they compete for this food source (Perissinotto and Pakhomov, 1988; Huntley et al., 1989). Atkinson et al. (2004) estimated the salp abundance at the peninsula to be 33 salps·m−2 in 1978, with an average abundance of 49.4 salps·m−2 from 1978-2003. Siegel et al. (2005) showed an average biomass of 12.17g·m−2 from 1981-2002 (range 0.76-75.23 g·m−2). 12.17t·m−2 was used as a starting biomass, but this was too high, so it was lowered to 8t·m−2 to balance the model. Pakhomov et al. (2002) noted that although salps have a short pulse of abundance, the P/B of an annual life cycle was likely between 1 and 3 based on studies by other researchers. However, this value was thought to be too low and was increased to 10y−1, Salps are generally ﬁlter feeders, whose biomass has been shown to increases in years associated with smaller phytoplankton (Moline et al., 2004; Dubischar et al., 2006). Diets of salps are composed of diatoms and ﬂag- ellates (von Harbu et al., 2011). Based in this the diet was set to, 10% micro-zooplankton, 30% cryptophytes, 11% copepods, 15% diatoms, 34% other phytoplankton.

Krill

Krill are a central link in the food web, as an important prey item for marine mammals, fish, and birds. In addition they are the only species in the model area to be fished commercially. Due to their importance in the food web, and the fishery operating on the older age classes, multi-stanza groups were created to represent the different life stages of krill. Multistanza groups are used to provide more detailed information about the life history of a species or species group within the model. Because predation is higher on adult krill, as some species target larger size classes (Lowry et al., 1998). For each multistanza group the mortality (Z) is entered

416 J.1. Model Parameters by Functional Group along with the biomass and consumption for the leading or oldest stanza group (Christensen et al., 2005). Diets for each multi-stanza group can be different and are entered in the diet matrix the same way for other functional groups. Within the model it is assumed that the species follow a von Bertalanffy growth curve where weight is proportional to length cubed (Christensen et al., 2005), with the growth parameter k used as an input to determining the biomasses of each stanza group. Biomass for the oldest group is entered and internal calculations of survivorship and biomass using the growth parameter K are calculated over monthly time steps to allow a more detailed resolution of age classes. The Von Bertalanffy k parameter has been estimated to be 0.478 for Euphausia superba at the Antarctic Peninsula and k=0.75 at South Georgia (Siegel, 1987; Reid, 2001). The value of 0.473 was used for the model in order to get a more accurate representation of biomass distribution of stanza groups. The krill model group representing Euphausia superba was broken down into four stanzas: The Krill Embryo stage represents the spawned eggs which sink to the meso- and bathypelagic, hatch and re-ascend as early larvae. Antarctic krill are broadcast spawners, releasing their eggs to sink to into the deep water where there is less predation. During decent eggs rely on the yolk sack for nutrients until about 425-1090m depending on temperature and geographic location (Hofmann et al., 1992). They do not feed during this stage, as they have carbon reserves that can last for roughly 26 days. This represents the Naupli and Metanauplii stages, before the gut and mouthparts have developed (Marr, 1962; Nicol et al., 1995; Arndt and Swadling, 2006). This stanza group ranges from month 0 to 1 month in age. For this group the diet was set to 100% imported, as these groups do not feed within the model, as they live off stored reserves. The krill Larvae stage is the first feeding stage of krill starting from calyptopis I (CI) where the mouth and guts develop. Phytoplankton is a critical resource for this stage, and timing of the bloom can affect the survival; generally if food is not found within 10-14 days the larvae cannot recover (Ross and Quetin, 1986). These surface dwellers pass through three

417 J.1. Model Parameters by Functional Group stages to become furcilia (where there are 6 stages), the duration of every larval stage being between 8 and 15 days (FAO, 2011). The krill larval stage in the model covers krill ages 1 to 6 months, with the next stage (juveniles) starting at month 7. This stage is somewhat dependent on sea ice, as larval krill located under the sea ice in the autumn and winter show better physiological condition than larvae in open water, and during low food conditions in the water column, larvae feed on ice algae (Meyer et al., 2002, 2009). Prey items for the larval stage include small copepods, protozoans, and autotrophic food sources, however they have the ability to switch to more heterotrophic food sources in the winter (Meyer et al., 2009). The diet for this group was set to 1% micro-zooplankton, 3% cryptophytes, 5% copepods, 4% diatoms, 65% Ice algae, and 22% other phytoplankton. The krill Juvenile stage represents krill has passed the last furculia stage and resembles the adult, although it is sexually immature (FAO, 2011). This starts in the model at 8 months, as it is estimated that it takes krill 85 days to reach the F3 phase (Ideka, 1984; Siegel et al., 2005), and then more time to reach the F6 stage. As furcilia develop into juvenile krill, they retain their association with the sea ice as they move into their second winter (Daly and Zimmerman, 2004). Juvenile krill are not targeted by the fishery, but they are often caught as bycatch when targeting the larger krill. The juvenile and adult stages also feed on phytoplankton during the ice free season and ice algae during the winter, being most abundant under the rough ice where they can access ice algae and hide from predators (Marschall, 1988). Feed- ing rates for juveniles and adults are lower in winter, as they reduce their metabolism and size in order to survive the winter Atkinson et al. (2002). Juvenile krill feed predominantly on phytoplankton, with diatoms being the most abundant item found in stomach contents of juveniles and adults (Atkinson et al., 2002; Schmidt et al., 2006). Other important prey items in the summer months include tintinnids (micro-zooplankton), large dinoflagellates, and other armored flagellates while copepods were considered rare (Schmidt et al., 2006). Juvenile and adult phases can switch to carnivorous food sources such as copepods (Cripps and Atkinson, 2000)Atkinson et al. (2002), most likely occurring when plankton biomass is reduced. Diel migra-

418 J.1. Model Parameters by Functional Group tions allow krill to feed on the meso-zooplankton community and helps them to avoid predation during daylight hours (Hernandez-Leon et al., 2001). The diet for this group was set to 2% macro-zooplankton, 2% micro-zooplankton, 1% cryptophytes, 18% copepods, 12% diatoms, 37% Ice algae, 3% other phytoplankton, and 25% detritus. The adult krill phase represents all sexually mature krill. Individuals mature and begin mating at two years of age (FAO, 2011), while some males do not reproduce until their third year (Siegel and Loeb, 1994). and can live up to seven years and grow up to 65cm (Reid, 2001). The krill ﬁshery operated primarily on this stanza group. Adult krill can reduce their metabolism and size in the winter to conserve energy (McGaﬃn et al., 2002; Meyer et al., 2010). Feeding studies at the onset of winter indicate the diet is dominated by small copepods with a general trend toward omnivory in the winter months (Atkinson et al., 2002; Meyer et al., 2010). The diet for this group was set to 1% juvenile krill, 0.001% larval krill, 0.001% krill embryo, 8% macro-zooplankton, 1% micro-zooplankton, 2% cryptophytes, 36% copepods, 12% diatoms, 35% ice algae, 3% other phytoplankton, and 2% detritus. The biomass of krill varies over years and seasons. For an area west of the Antarctic Peninsula estimates for the 1993-1994 season range from spring (32 g·m−2) summer (95 g·m−2) fall (12 g·m−2) and winter(8g·m−2) (Lascara et al., 1999). Elephant Island showed a low biomass of 0.98 g·m−2 for the 90/91 summer to a high of 31.16 g·m−2 for the 77/78 season (Siegel et al., 1998). Various samples Antarctic wide are summarized in (Siegel et al., 2005) with biomass at the peninsula ranging from 8-138g/m2 depending on the year and method of sampling (acoustic vs. net). A summary of multiple krill samples spanning the Antarctic in the krill/salp database (Atkinson et al., 2004), estimated the Antarctic Peninsula biomass to be 37.66g·m−2 in 1978. While this estimate likely only represents the adult and juvenile stages, the leading or adult krill biomass was set to 9.080t·m−2, so that the total krill biomass was 35.22t·m−2. Krill can live up to and in some cases more than 6 years (Pakhomov, 1995a). Mortality ranged from 0.52y−1 for mature stages of krill, to 1.1y−1

419 J.1. Model Parameters by Functional Group for the first year, 2.41y−1 for the last years of life. Survival at the Antarctic Peninsula averaged 0.36-0.41y−1 (for age classes 2+), but can range from 0.4-0.78y−1. At south Georgia krill grow at high rates from October- March (austral summer) indicating growth rates are higher than predicted by existing models (Reid, 2001). Based on the values in table J.7, the P/B values used for krill groups were set to; 1.5y−1 for adults, although higher than the natural mortality rates from other areas, it was increased to account for fishing. The juvenile group was set to 0.9y−1, and accounts for a small amount of fishing mortality. The Larvae group was set to 2.5y−1 and the embryo class was set to 8y−1, higher than the year 1 values, but since these age classes are so short, and highly reliant on environmental conditions, it was assumed they would have higher mortality rates than krill that reach the juvenile phase.

Table J.7: Natural mortality rates (y−1) of Antarctic krill (Euphausia superba) for areas north and south of the Antarctic divide (AD) for the Cos- monaut and Cooperation Seas. Values taken from Pakhomov (1995b).

Age S of AD Coopera- N of AD Coopera- Cosmonaut Sea N tion Sea tion Sea and S AD 1 1.1 1.12 1.09 2 0.65 0.64 0.65 3 0.55 0.52 0.57 4 0.7 0.54 0.77 5 1.29 0.95 1.54 6 - 2.41 -

Consumption rates were calculated to be of 5% of body carbon per day based on fecal pellets or 0.4-1.7% of body carbon from gut ﬂorescence, from Feb-March at South Georgia (Pakhomov et al., 1997). Over a 100 day growing season this could range from 40-500y−1. These were from the 38- 42mm length indicating they were of the adult size class. As a conservative estimate the Q/B for adult krill was set to 33y−1. In addition to the curvature parameter (Von Bertalanﬀy growth K parameter), a recruitment power parameter was set to 1. Lower values 0.1-0.5 indicate juveniles spend time outside the model area where density depen-

420 J.1. Model Parameters by Functional Group

Table J.8: Multistanza parameters for krill functional groups.

Group Start Age (months) B Z Q/B 51 Krill Larvae 0 0.006 8 698.506 52 Krill Juvenile 2 0.879 2.5 149.443 53 Krill sub-adult 8 25.26 0.9 49.481 54 Krill Adult 36 9.08 1.5 33 dence may affect mortality (Christensen et al., 2005). As krill are spawned and hatched within the model area, the value was set to the default of 1. A weight at maturity (WM ) vs weight at length infinity (W∞), the weight of fish at the asymptotic length (L∞), is included with weight at length infin- 3 ity equal to the length at size infinity cubed or W∞ = L∞ . L∞ was set to 65mm (Reid, 2001), with length at maturity set at 37.5mm based on female 22 krill reaching L50 at 34.65-35.9mm and males reaching L50 43.35-43.71mm (Siegel and Loeb, 1994) to give a ratio of 0.190.

Macro-zooplankton and Other Meso-zooplankton

The macro-zooplankton group contains all zooplankton larger than the 0.2mm size with the exception of krill (Euphausiids), salps (tunicates), and copepods. Noted in literature were Ostracods, Amphipods, Mysids, Ctenophores, Cnidarians, Polychaetes, Chaetognaths, Molluscs, and various larvae (Hop- kins, 1985; Calbert et al., 2005). Macro-zooplankton samples from Ger- lache Strait and Bransﬁeld Strait within the model area indicate macro- zooplankton biomass ranging from 0.141-6.99g·m−2 (Calbert et al., 2005). Zooplankton groups(meso and macro) from Croker Passage in 1983 were estimated to be 19.07g·m−2 (Hopkins, 1985). While these estimates represent values in the summer when biomass is higher, the annual value was set to 8.170t·km−2. The EE was set to 0.95, and the P/Q was set to 0.3 to allow the model to estimate the PB and QB values. Diet from other studies: suggest a variety of food sources including ice algae, other phytoplankton, and smaller

22 L50 is deﬁned where 50% of the population reaches sexual maturity

421 J.1. Model Parameters by Functional Group zooplankton (Moline et al., 2004; Peck et al., 2005). The diet for this group was set to: 2% adult krill, 4% juvenile krill, 5% micro-zooplankton, 10% cryptophytes, 2% copepods, 21% diatoms, 35% ice algae, 15% other phytoplankton, and 6% detritus.

Micro-zooplankton

Micro-zooplankton is thought to be an important part of Antarctic food webs, and a source of prey for krill (Froneman et al., 1996). Surveys of nano and micro-zooplankton from the Weddell sea in summer indicate levels of 0.3-0.6gC·m−2 (or 2.49-4.98g·m−2) (Garrison et al., 1991). The biomass for the model was set to a conservative value of 2.9t·km−2. The the Q/B was set to 110y−1, slightly higher than the copepod value, with an assumed P/B value of 65y−1. The diet was assumed to be 15% cryptophytes, 25% diatoms, 20% ice algae, 35% other phytoplankton, and 5% detritus.

Copepods

This group includes numerous species of copepods (see Hopkins, 1985, for a detailed list of copepod species). Copepods are an abundant zooplankton species in the Antarctic, and serve as a food source for krill, other zooplankton, ﬁsh, and even birds. Biomass of copepods was sampled at 15.14g·m−2, in South Georgia and from 4.53- 23.12g·m−2 in the Bellingshausen Sea (Cal- bert et al., 2005). Estimates at South Georgia range from ¡1 to 13 g·m−2 for one species C. acutus (stages CIV and CV only) should be considered low, as these stages are thought to only represent 25% of the total copepod biomass at South Georgia (Shreeve et al., 2005). The model biomass was set to 15.2g·m−2 for all copepod species, based on samples from South Georgia. The P/B ratio from South Georgia was estimated at about 10y−1 for CIV and CV stages of C. acutus based on Shreeve et al. (2005), although this parameter was ultimately estimated by the model. Consumption from daily uptake rates indicate a range from 2.5-5.4% of body weight per day as measured by carbon, however values for the Southern Ocean can range from ¿1-20% of the body weight per day for various copepod species (Metz

422 J.1. Model Parameters by Functional Group and Schnack-Schiel, 1995). When converted to annual rates, consumption of 1-20% of body weight a day would be 3.65 to 73, although copepods are not actually feeding every day of the year. It is likely that smaller copepods not included in the study would have higher annual Q/B rates, but the group Q/B was set to 50y−1. The EE value was set to 0.95. Studies of Calanoides acutus, Rhincalanus gigas and Calanus propinquus indicate diet is primarily comprised of protozoans, micrometazoans, autotrophs and can include other zooplankton (Bathmann et al., 1993)Metz and Schnack-Schiel (1995). The diet was set to: 15% micro-zooplankton, 35% diatoms, 25% ice algae, 20% other phytoplankton, and 5% detritus.

Primary Producers

Primary producers were split into four groups in order to account for their different roles in the food web. Research has identified the linkages between cryptophytes blooms and lower salinity water, as well as diatoms and higher salinity waters (Moline et al., 2000, 2004). Diatoms and cryptophytes have been shown to be the dominant phytoplankton for the region in the summer months with diatoms having a strong association to sea ice (Varela et al., 2002; Garibotti et al., 2003; Moline et al., 2004), thus demonstrating their importance to the food web. With the intent of exploring how the different types of producers affect the system as a whole and how these issues relate to krill, salps, and other consumers in the food web, primary producers were split into cryptophytes, diatoms, other phytoplankton, and ice associated algae. All producer groups are considered to be associated with open water with the exception of the sea ice. Biomass for phytoplankton was given for summer months. Annual average values needed for model input were assumed to be 1/3 of the summer biomass. In addition production values were calculated annually, but based on 120 day growth period (Smith et al., 1998), to account for the high seasonality of the area.

423 J.1. Model Parameters by Functional Group

Cryptophytes

Cryptophyte abundance has been shown to be correlated with lower salinities in the Antarctic Peninsula (Moline et al., 2000), making it a potentially critical base for the food web in the event that climate change increases or continues in the future. Biomass ranges for this group were as high as 21.6t·km−2 for summer values in highly concentrated areas (Varela et al., 2002), but reduced to 5.4t·km−2 when accounting for the whole study region. Others (Garibotti et al., 2003) estimated the summer biomass to be roughly 6t·km−2 for the summer season. The average yearly biomass was set to 2.2t·km−2. The production for this group was set to 75 y-1 based on a 120 day summer season for growth with published production rates ranging from 0.5-1.5 g C·km−2 · day−1 (Varela et al., 2002), however it was increased to 80y−1 to balance the model.

Diatoms

This group contains all diatoms not associated with the sea ice. The biomass was sampled to range from 130 ug C·l−1 (Garibotti et al., 2003) and was converted to a summer biomass range of 40.9g·m−2 (wet weight). The annual biomass was reduced to 1/3 of the summer biomass to give 13.65t·km−2, which was slightly lower than the regional average of roughly 21t·km−2 for the WAP region calculated by (Varela et al., 2002). The ﬁnal value used for the model was set to 17.41t·km−2 to balance the model. The production of diatoms was converted from 0.87-4.54 g C·m−2 · day−1 (Varela et al., 2002) to give a P/B range of 22.5-117.4y−1. The value of 90.51y−1 was used to balance the model.

Ice Algae

This group contains all phytoplankton which is associated with the sea ice. Species known to exist in the ecosystem are chrysophytes, diatoms, dinoﬂagellates, cryptophytes, ciliates, choanoﬂagellates, prasinophytes, and prymnesiophytes (Garrison and Buck, 1989), as well as bacteria. Biomass

424 J.1. Model Parameters by Functional Group estimates were converted from chl a to wet weight using conversions provided by Cauﬀope and Heymans (2005). A late winter biomass of 3.2t·km−2 was provided by Kottmeier and Sullivan (1987) was slightly lower than the 5.67t·km−2 estimate from Smith et al. (1998). Based on the winter chlorophyll concentration in ice cores and newly formed ice Garrison and Buck’s 1989 estimate of roughly 21t·km−2 is still lower than the highest reports that Chl a concentrations can be as high as 0.4g·m−2 or about 140g·m−2 (Arrigo et al., 1997). The average yearly biomass was set to 25t·km−2. Winter production values for ice algae ranged from 0.017gC·m−2 · day−1 (Lizotte, 2001) to 0.035gC·m−2 · day−1 ((Kottmeier and Sullivan, 1987) to 1gC·m−2 ·day−1 (Arrigo et al., 1997). Summer production values were much higher at 1.6gC·m−2 · day−1 (Smith et al., 1998). At the maximum summer production values of 1.6gC·m−2 ·day−1 (for 120 days of summer) would yield an annual rate of 69.12y−1, while winter rates of 0.017gC·m−2 · day−1 (for 245 days) would yield an annual rate of 1.49y−1. A value of 45.00y−1 was used for the Ecopath model.

Other Phytoplankton

The other phytoplankton group contains all primary producers not associated with the sea ice with the exception of diatoms and cryptophytes. This included chlorophytes, dinophytes, crysophytes, unidentiﬁed phytoﬂag- ellates, and bacterial contributions to primary production, generally present in the summer months. The average annual biomass was set to 5.5t·km−2 based from a summer value of 27.9ugC·l−1 (Garibotti et al., 2003). P/B increased from the calculated value of 77.4y−1 (from 0.21gC·m−2 · day−1) (Varela et al., 2002) to 105y−1 to balance the model.

Detritus

Detritus biomass was calculated using the following equation from Pauly et al. (1993):

Log10D = −2.41 + 0.954Log10PP + 0.863Log10E (J.8)

425 J.2. Ecosim Input Parameters

where D is the standing stock of detritus (g C·m−2), PP is primary productivity (g C·m−2 · y−1), and E is the euphotic depth in meters. Estimates of primary production for the area ranged from 0.36gC·m−2 · y−1 (Vernat et al., 2008) for oﬀshore areas to 55-425gC·m−2 · y−1 (Smith et al., 2001) for areas near Palmer Station. A primary production value of 0.4gC·m−2 · y−1 was used to calculate the detritus biomass along with a photic depth of 25 meters based on the depth of the upper mixed layer ranging from 13-23m for the 1995-1996 summer (Varela et al., 2002) and 30-35m for later in the 1996 summer season (Garibotti et al., 2003). This resulted in a detritus estimate of 3.43t·km−2 of detritus.

J.2 Ecosim Input Parameters

Fisheries

Krill Fishery

For this model the ”krill fishery” is classified as mid-water otter trawls as cited in the CCAMLR statistical Bulletin (CCAMLR, 2008b). Catches (figure J.3) were provided and applied to adult and juvenile krill groups as the mesh size of the trawls is not capable of catching the smaller size classes. Krill fishing in the AP show that most catches are obtained from the shelf area in depths less than 1000m (Murphy et al., 1997), where they are likely competing with land based marine mammals and birds. Effort for this fishery was driven using the total number of fishing hours (figure J.2). However in the fitting process, catch time-series was used as forced values, thereby negating the effort driver.

Other Fishery

The ”other fishery” includes all other species caught over the time period of the model 1978-2007. This includes exploratory fishing for toothfish species, and general fishing that occurred on any species other than krill in this area. Catches for the first year of the model were set to 1E-05t·km−2 for each of the following groups in which at least one species was fished throughout

426 J.2. Ecosim Input Parameters the time series; Other Icefish, Toothfish, Large Nototheniidae, Small No- totheniidae, Myctophids, Other Pelagics, C. gunnari, P. antarcticum, and N. gibberifrons. This value was set low, as there were no recorded catches in 1978, however 1979 had the highest landings and effort for the entire time-series. This fishery mostly includes test fisheries on finfish species with some by-catch. As all species caught in the test fishery are reported, and broken down by species. Effort (fishing hours) was used to drive the catches of these species (figure J.2), however, this did not reproduce the pattern of catches (figure J.3) for the various fish species, so catches were entered for each functional group and used in the fitting process. All fishery data was obtained from CCAMLR records on the digital database (CCAMLR, 2008b).

25000 3500 Krill Fishery 3000 20000 Other Fishery 2500

15000 2000

1500 10000

Krill EﬀortKrill (Hours) 1000

5000 OtherFishery Eﬀort (hours) 500

0 0 1978 1983 1988 1993 1998 2003

Figure J.2: Krill fishing effort used in model fitting for the krill fishery and the other fishery representing fish catches. Data provided by CCAMLR (2008b).

Abundance Trends

In addition to krill catch and eﬀort, biomass and abundance trends were provided by multiple sources for varying time spans. These trends were used to ﬁt the model using either abundance (Atkinson et al., 2004; Quetin

427 J.2. Ecosim Input Parameters

50 12 45 Fish Krill 10 40 35 8 30 25 6 20 4 15 Fish Catch Catch Fish (1000tonnes) 10 catch Krill (10000tonnes) 2 5 0 0 1974 1979 1984 1989 1994 1999 2004

Figure J.3: Krill catches used in model fitting for the krill fishery and the other fishery representing fish catches. Data provided by CCAMLR (2008b). and Ross-Quetin, 2006), or biomass (Siegel et al., 1998, 2002). However ultimately the KRILLBASE (Atkinson et al., 2004) data was used, as it provided the most complete geographic and temporal time-series trend for krill (figure J.4). For salps, two potential data sets were available for abundance trends; the KRILLBASE dataset (Atkinson et al., 2004), and a dataset from PALMER station (Quetin and Ross-Quetin, 2006). Again the KRILLBASE dataset was chosen as it was more complete (figure J.5) . Adelie, Chinstrap and Gentoo penguin abundance trends were taken from the Palmer Long Term Ecological Research Data (Fraser 2006), based on the number of breeding pairs around Palmer Station on Anvers Island, Antarctic Peninsula. While adelie penguins have occupied Palmer Station at the Antarctic Peninsula for over 700 years, the first chinstrap colony at Palmer Station was established in 1974, and the first gentoo arrival was not until 1994 (McClintock et al., 2008). Each penguin species has a different relationship to the climate, sea ice, and the changes in food availability. For example, it is believed chinstrap and gentoo penguins avoid areas with per- sistent sea ice as a majority of their populations are based in sub-Antarctic

428 J.2. Ecosim Input Parameters

300 80

70 Atkinson et al. 250 2004 (#/m2) 60 200 PALMER (#/m2) 50

150 40 Siegel et al. 30 1998 (g/m2) 100

Krill Biomass Biomass Krill (g/m2)

Krill Abundance Abundance Krill (#/m2) 20 Siegel et al. 50 2002 (g/m2) 10

0 0 1978 1983 1988 1993 1998 2003

Figure J.4: Krill abundance and biomass trends from the Antarctic Penin- sula

200

Atkinson 150 et al. 2004 (#/m2)

100 PALMER (#/m2) 50

Salp abundance abundance Salp (#/m2)

0 1978 1983 1988 1993 1998 2003

Figure J.5: Salp abundance trends from the Antarctic Peninsula.

429 J.2. Ecosim Input Parameters areas, and they most likely evolved in conditions with open water a majority of the year (McClintock et al., 2008). Adelie penguins on the other hand, are quite dependant on winter sea ice through the krill that is supported by the ice. It has been suggested that at the Antarctic Peninsula sea ice has declined past an optimum point for adelie penguins, and this is the cause for the declining population (Croxall et al., 2002). Emperor penguins have also been shown to decline as much as 50% since the 1970 in eastern Antarctica (Terre Adelie; Indian Ocean sector) which has been correlated to reduced sea ice in the same area (Barbraud and Weimerskirch, 2001), however datasets for emperor penguins are lacking for the Antarctic peninsula.

Table J.9: Summary of time-series data used to ﬁt the model.

Time series data Type of data used Reference Krill Abundance Relative Abundance Atkinson et al. (2004) Krill Catch Forced Catches CCAMLR (2008b) Krill Effort Effort CCAMLR (2008b) Salp Abundance Relative Abundance Atkinson et al. (2004) Other Fishery Catch Forced Catches CCAMLR (2008b) Other Fishery Effort Effort CCAMLR (2008b) Adelie Abundance Relative Abundance Fraser (2006) Gentoo Abundance Relative Abundance Fraser (2006) Chinstrap Abundance Relative Abundance Fraser (2006)

Forcing Functions

Three forcing functions (FF) were used to fit the model: sea surface temperate (SST), sea ice cover (% cover), and the southern oscillation index (SOI). The SST and ice cover time-series were extracted from the HadISST (Hadley Centre Sea Ice and Sea Surface Temperature data set) model by month (BADC, 2010) for cells within area 48.1. The model data is presented as the monthly average for 1◦x 1◦ cells for the world, with the values for the Antarctic Peninsula used as the mean of all cells within the area (figure 3.3). The Southern Oscillation Index (SOI) used in the model is calculated using the difference in air pressure between Tahiti and Darwin, Australia. Pos-

430 J.2. Ecosim Input Parameters itive values indicate cold ocean temperature, higher air pressure in Tahiti, and lower air temperature in Darwin. Negative values indicate, lower air pressure in Tahiti, higher air pressure in Darwin, and warmer waters. Posi- tive values are generally associated with La Nina years, while negative values are associated with El Nino years. SST is also affected by the changes in pressure, however the SOI may give better insight as to factors determining salp abundance and was therefore tested as a driver. Values for the SOI (figure 3.3) were taken from the PALMER station dataset (Stammerjohn, 2007). All forcing functions were re-scaled so that the average of the first year of the model (1978) was scaled to 1. Ice cover was used as a FF for ice algae within the model, as well as diatoms. Ice algae remain in the sea ice overwinter and are utilized by predators such as krill throughout the winter (Marschall, 1988; Arrigo et al., 1997). Diatoms are favored in cooler years associated with higher sea ice, and are often an important component of sea ice algae, forming blooms at the ice edge when melting commences (Legendre et al., 1992). Ice cover as a forcing function for both of these functional groups provided a better fit (reduction to sum of squares value) to the krill functional groups. In addition sea ice was used as a FF for ice algae predators, applied to the arena area for each predator. The ecological interpretation is that as ice cover increases, so does the arena area for predators to feed on ice algae. SOI and SST were used under different fitting attempts (A and B respectively). Forcing functions (FF) for cryptophytes and the other phytoplankton functional groups, as cryptophytes have higher biomass in warmer years (Moline et al., 2004), and the other phytoplankton group was created to represent species associated with the spring bloom. The SST pattern follows a similar pattern to summer bloom and ice free conditions important to warmer water producers. Salps tolerate warmer water than krill (Atkinson et al., 2004), with higher prevalence of salps potentially linked to warming waters being advected in the area (Pakhomov and Froneman, 2004). By ap- plying these forcing functions to the cryptophytes and other phytoplankton functional group, we were able to fit the time-series of salps to model. Other environmental time-series were tested in the fitting of the model,

431 J.2. Ecosim Input Parameters but did not produce optimal results. Data from the PALMER LTER study of sea ice extent, and open water extent, and air temperature were considered (Stammerjohn, 2007). While sea ice extent did provide comparable results (once both FF were re-scaled to average 1 for the ﬁrst year) to the ice cover FF, future data is available for percentage ice cover, therefore it was selected over ice extent.

Mediation Functions

While forcing functions were helpful in fitting the model to past data, mediation functions were added to decrease SS values for both fittings, and include indirect ecological relationships. A mediation function was also applied to krill to represent the protection sea ice can provide from predators (figure J.6). Krill have been observed by SCUBA divers to retreat into crevasses in sea ice for protection (Marschall, 1988). A mediation function was created so that as the biomass of ice algae increases, krill become less vulnerable to their predators, with a large decline as ice decreases from the starting values within the model, and tapering impacts from low to extremely low ice cover. This mediation function was applied to both the larval and juvenile stages of krill under both fitting scenarios (SOI and SST). Sea ice was also used in a mediation function for salps. As salps are pelagic organisms with the abundance higher in warmer years with lower sea ice (Moline et al., 2004; Nicol, 2006), the mediation function used indicated as sea ice decreased (as determined by ice algae), the foraging area of salps increased using a linear relationship (figure J.7). This mediation function was applied to all prey groups of salps under both fitting scenarios (SOI and SST).

432 1

Relative Weight Relative 0.5

Ice Algae Biomass

Figure J.6: Mediation function for larval and juvenile krill. As ice algae biomass increases krill groups become less vulnerable to predators. 433 15

Relative Weight Relative 3

Ice Algae Biomass

Figure J.7: Mediation function used for the salp functional group. As ice increases (as determined by the biomass of ice algae) the foraging area for salps decreases. This was applied to all prey groups of salps. 434 J.2. Ecosim Input Parameters

A mediation function to replicate salps dying at high food concentrations was tested to see if it would improve the fit. Therefore a mediation function was used for salps based on the idea that at high food concentrations the mucous nets which are used for feeding become clogged with food particles. This renders the salps unable to continue feeding and causes death in lab experiments for the salp, Pegea confoederata (Harbison et al., 1986). A mass stranding of Salpa thompsoni near the Antarctic Peninsula in 2002 was linked to high wind conditions transporting nutrients and re-suspending detritus, thereby causing high particle concentrations and leading to the clogging and death of the salps (Pakhomov et al., 2003). The mediating group for salps was chosen to be the other production group, as this and cryptophytes were driven by temperature or SOI, depending on the fitting scenario. As cryptophytes are smaller in size, and generally less abundant in the model on an annual basis, it was assumed the larger more abundant other phytoplankton group would do more damage to clogging salps. The mediation function was applied to the search rate of salps on other phytoplankton and cryptophytes, so as the biomass of other producers increase, the search rate will also increase to a certain point and then drop off. This pattern was selected, as not all salps become clogged at the same food concentrations (Harbison et al., 1986). While this mediation function did improve the SS value initially, the sea ice mediation function provided a lower SS value. The combination of both mediation functions of salps did not decrease the SS value lower than the sea ice mediation function alone, so the clogging function was removed from the model.

Biomass Accumulation

Biomass accumulation was added to the chinstrap and gentoo penguin groups, based on increases to populations in the model area. Please refer to the individual functional group descriptions for values and ecological relevance.

435 J.2. Ecosim Input Parameters

Group info Parameters

The maximum relative feeding time is the amount of time a predator can increase their foraging time if prey becomes scarce. The default value for functional groups is set to 2, but can be increased for species which are able to increase their (Christensen et al., 2005)(Christensen et al., 2007). The value was increased for land based predators, as they can increase their foraging time by spending less time on land. A value of 10 was used for whales (killer, sperm, blue, ﬁn, minke and humpback), and a value of 5 was used for seals (leopard, Ross, Weddell, crabeater, southern elephant and Antarctic fur seal). The feeding time adjustment rate parameter was set to a default value of 0, indicating a constant feeding time (along with a constant risk to predation). This parameter can range from 0 to 1, with 1 indicating fast responses in adjusting feeding times as to stabilize the consumption (Q/B) (Christensen et al., 2005, 2007), meaning a predator respond faster to feed more in lower food concentrations as to regulate the Q/B ratio set in the model. A recommended value of 0 was used for all model groups with the exception of marine mammals and birds. The recommended value for marine mammals is 0.5, which was used for all whale and seal functional groups. A value of 0.2 was used for penguins and ﬂying birds, as they can regulate the amount of time spent in the water foraging.

Vulnerabilities

Vulnerabilities were estimated by Ecosim using the fit to time series routine (Buszowski et al., 2009). This routine searches for vulnerabilities which lower the overall sum of squares. Further manipulation of key predator prey interactions was done to see if model fit was improved. In cases where adjustment of individual interactions provided a better model fit, the adjusted values remained. It should be noted that several iterations of the vulnerability search and manual manipulation of vulnerabilities was done under a variety of forcing functions and time series data (various krill and salp trend, SOI, sea ice cover, sea ice extent, SST, and air temperature) before

436 J.3. Model Parameterization and Output

ﬁnal selections were made. Final vulnerabilities are presented in appendix K.

J.3 Model Parameterization and Output

Ecopath Model Balancing

In the Ecopath phase, changes were made to parameters in order to ensure the model could be balanced before moving onto the Ecosim portion. General changes made to the model were:

1. The consumption rates of some marine mammals were too high, and had to be lowered in the balancing process. The high consumption values calculated caused the EE for predators such as other marine mammals, ﬁsh, and penguins to be over 1. In most cases the Q/B value reduction was small (less than 10%).

2. The P/B ratio for ﬁsh was too low as estimated by Pauly (1980). As the empirical data used to formulate this equation was based on temperate and tropical ﬁsh species and excluded polar data, it most likely underestimates the value for polar species (Palomares and Pauly, 1998). Values were increased to balance these model groups.

3. Literature indicates a very strong dietary link between predators and krill. However, even as though krill biomass (for all stages combined) was large in comparison to other organisms, the contribution to the diet of predators had to be decreased in order to balance the model.

4. The consumption of cephalopods was initially guestimated to be 10y−1 (Efran and Pitcher, 2005), but was lowered as the predation mortality on prey items was too great. It was lowered to 2y−1 in line with the cephalopod value for the Kerguelen Islands, a sub-Antarctic area (Pruvost et al., 2005).

5. Changes to the P/B and Q/B values for invertebrates. Most alterations to calculated values were increases in order to balance the

437 J.3. Model Parameterization and Output model.

438 J.3. Model Parameterization and Output

Table J.10: Balanced model with bolded values estimated by the model.

Group name Trophic Biomass P/B Q/B EE P/Q level (t·km−2) Killer Whales 4.543 0.001 0.05 11 0 0.005 Leopard Seal 4.139 0.006 0.12 8.1 0.637 0.015 Ross Seal 4.123 0.004 0.13 15.3 0.83 0.008 Weddell Seal 3.972 0.021 0.17 13.88 0.689 0.012 Crabeater Seal 3.423 0.164 0.09 15.86 0.363 0.006 Antarctic Fur Seals 3.694 0.028 0.175 25 0.862 0.007 S Elephant Seals 4.25 0.006 0.165 10.37 0.437 0.016 Sperm whales 4.203 0.005 0.034 7.33 0 0.005 Blue Whales 3.41 0.001 0.032 3.53 0.683 0.009 Fin Whales 3.441 0.003 0.035 4.12 0.524 0.008 Minke whales 3.27 0.065 0.064 6.34 0.91 0.01 Humpback whales 3.343 0.02 0.04 4.12 0.963 0.01 Emperor penguins 3.871 0.005 0.15 28.69 0.933 0.005 Gentoo Penguins 3.93 0.007 0.22 29 0.642 0.008 Chinstrap Penguins 3.917 0.005 0.33 34 0.696 0.01 Macaroni Penguin 3.67 0.014 0.3 25 0.373 0.012 Adelie Penguins 3.518 0.034 0.29 30 0.793 0.01 Flying birds 3.697 0.19 0.34 14.88 0.95 0.023 Cephalopods 3.404 2.49 0.95 2 0.653 0.475 Other Iceﬁsh 3.689 0.337 0.38 1.57 0.726 0.242 Toothﬁsh 4.228 0.046 0.165 0.77 0.627 0.214 Lg Notothenioids 3.335 0.59 0.37 1.95 0.452 0.19 Sm Notothenioids 3.332 0.341 0.65 2.2 0.873 0.295 Shallow Demersals 3.375 0.031 0.75 4.125 0.362 0.182 Deep demersals Lg 3.684 0.042 0.29 2.18 0.803 0.133 Deep demersals Sm 3.687 0.08 0.65 2.7 0.82 0.241 Myctophids 3.263 0.185 1.35 3.73 0.882 0.362 Other Pelagics 3.776 0.49 0.55 2.02 0.838 0.272 C. gunnari 3.391 0.29 0.48 1.8 0.475 0.267 P. antarcticum 3.269 1.25 1.1 3.55 0.603 0.31 N. gibberifrons 3.199 0.81 0.41 1.55 0.645 0.265 Mollusca 2.129 9.5 0.639 2.556 0.608 0.25 Table J.10 Continued on Next Page

439 J.3. Model Parameterization and Output

Table J.10 Continued Group name Trophic Biomass P/B Q/B EE P/Q level (t·km−2) Salps 2.227 8 10 33.333 0.01 0.3 Urochordata 2.135 5.05 0.234 1 0.554 0.234 Porifera 2 12.719 0.159 0.795 0.815 0.2 Hemichordata 2 0.045 0.375 2 0.534 0.188 Brachiopoda 2.158 0.028 0.898 4.5 0.59 0.2 Bryozoa 2.108 0.491 0.475 1.75 0.98 0.271 Cnidarians 2.438 1.531 0.25 1 0.982 0.25 Crusteceans 2.374 3.613 1.05 4.2 0.888 0.25 Other Arthropods 2.929 1.01 0.616 3.326 0.981 0.185 Worms 2.438 12 0.7 3.2 0.84 0.219 Echinoidea 2.732 4.33 0.116 0.464 0.774 0.25 Crinoidea 2.428 0.164 0.125 0.8 0.523 0.156 Ophiuroidea 2.479 6.76 0.45 1.8 0.551 0.25 Asteroidea 2.345 1.778 0.231 0.924 0.774 0.25 Holothuroidea 2 5.45 0.316 1.1 0.938 0.287 Krill Adult 2.529 9.08 1.5 33 0.672 0.045 Krill Juvenile 2.25 25.26 0.9 49.481 0.788 0.018 Krill Larvae 2 0.879 2.5 149.443 0.011 0.017 Krill Embryo 2 0.006 8 698.506 0.237 0.011 Macro-Zoopl 2.154 8.17 7.577 25.257 0.95 0.3 Micro-Zoopl 2 2.9 65 110 0.982 0.591 Cryptophytes 1 2.2 80 - 0.983 - Copepods 2.15 15.2 26.066 50 0.95 0.521 Diatoms 1 17.41 90.51 - 0.396 - Ice algae 1 25 45 - 0.874 - Other Phytopl 1 5.5 105 - 0.806 - Detritus 1 3.43 - - 0.176 -

Ecosim Fitting

The model was fit under 2 conditions: The first fitting (A) used SOI to drive cryptophytes and the other production group, and the second fitting

440 J.3. Model Parameterization and Output

(B) used temperature to drive cryptophytes and other production. For both attempts at fitting the model, there was no difference to the fit of penguin groups. Declines in adelie penguins were captured through the decline of the main prey item krill. For the chinstrap and gentoo, obtaining increases in the population while food sources (krill, cephalopods, and fish). Based on increases in both populations documented, a biomass accumulation rate was added for both of these species. A rate of 5.7% a year (0.057) was used for gentoo penguins, based on increases of 5.7% at Cierva Point on the Antarctic Peninsula, and a nearly 50 fold increase at PALMER Station on Anvers Island (Quintana and Cirelli, 2000; Fraser, 2006). Even with the addition of a biomass accumulation rate in the model, the population still shows small declines. The same is true for chinstrap penguins, even with a modest biomass accumulation rate of 10% a year, the model is not capable of capturing the data recorded from PALMER station as the surveys indicate the number of breeding pairs increased from 28 to 1288 between 1996-2004 (Fraser, 2006). In the early 1990s it was thought there was an increase in chinstrap penguins in the region due to a surplus of krill caused declines in other krill predators such as baleen whales, with some colonies increasing 6-10% per year or even higher (Fraser et al., 1992). Surveys from other breeding locations indicate mixed changes in populations; of the three study sites, one population increased, one decreased, and one fluctuated from 1980-2000 (Croxall et al., 2002) indicating the data used from PALMER station may in fact not be representative of the entire model area. Krill were fit to the model using the mediation function for sea ice (figure J.6), and through the use of sea ice as a driver of their main food sources, sea ice algae and diatoms in addition to protection from predators. Krill abundance has been shown to be higher in years with lower sea temperature, higher sea ice extent, and higher nutrient concentrations, while the opposite patterns are observed for salps (Lee et al., 2010). Although the peak in biomass for 1983 was not captured in the model for adult krill, juvenile krill show a higher biomass than adult krill in this year. While some juvenile krill are likely caught in the samples provided by this dataset, as the adult

441 J.3. Model Parameterization and Output group is classified by sized 35mm and larger, neither group shows the highest biomass in this year. The highest adult krill biomass is shown in the model for 1992 at just over 23t·km−2 while the highest biomass for juveniles was in 1988 at just over 58t·km−2 for 1988, the highest biomass projected by the model for any krill group. Krill trend data indicates high biomass in 1992 and 1996, although adult krill in the model does not show high biomass in these years. Juvenile krill does have a relatively high biomass in 1996, but not 1992. The greatest differences between the two fitted models arises from the groups where SST and SOI were used as forcing functions: cryptophytes and the other phytoplankton group. In addition salps show differences between the two fittings. For cryptophytes, both models show peaks in abundance in 1987 and 1992, however values are higher under the SST fitted model. The other phytoplankton group shows the same general trends for both fitted models, however peak abundances are higher under the SST fitted model, and low values are more extreme under the SOI fitted model. Salps show differing trends under the two fitting attempts. Under the SST fitted model a peak in biomass for 1989 is lower than for the SOI fitted model. Also the SOI fitted model generally has higher values after 1999 compared to the SST fitted model. The ending biomass for the SOI fitted model is higher for the salp group. While the SOI fitted model visually appears to fit the salp trend data better, it has been suggested recently in the literature that salp trends from 1998 onward are thought to have stabilized showing mid range abundances in recent years when compared to data from 1975-2002 (Lee et al., 2010). This is different to the data used for the model (Atkinson et al., 2004) which still shows fluctuations in salp biomass past 1998 (figure J.5). Krill and salp abundance is thought to be strongly influenced by the SOI, the ACW (Antarctic Circumpolar Wave) which brings cold deep water the surface at the peninsula, and the placement of the sACCf (Southern Antarctic Circumpolar Current Front) (Lee et al., 2010). Salp abundance has been shown to have a strong negative correlation to sea ice extent in the previous winter, which is negatively correlated to SOI (Loeb et al., 2009). SST was tested to fit the model as it is a contributing factor to both the ACW

442 J.3. Model Parameterization and Output and sACCf, although there are many other important factors contributing to the dynamics of these environmental drivers. SS values for the SST fitted model was 68.57, and SS for the SOI fitted model was 78.95. Ultimately it was decided that the SST driver provided a better fit based on SS values, with biomass trends for most species being similar (See appendix P for graphs of all functional groups).

Monte Carlo Parameter Estimation

Estimates of all parameters in the Monte Carlo routine are provided in appendix N. A summary of the biomass values obtained are provided in table J.11 along with mean and 95% CI. Graphs for biomass are provided in appendix O. CV values were assigned based on pedigree ranking of input data (Christensen et al., 2005), and are provided in appendix M. 1000 iterations were unable to improve SS value, however they did provide ranges of acceptable input parameter values. While the CV values for marine mammals was set to 0.7 (with the exception of Ross seals), some species showed higher ranges of acceptable input parameter values. In general the model was able to support a larger range of biomass for species with higher initial biomasses (Weddell seals, crabeater seals, fur seals, minke whales and humpback whales). Ranges for penguin groups was relatively low, although the model is able to support a much higher biomass of flying birds, despite their staring biomass being higher than penguin groups. Fish groups share the same CV value, with the general trend that biomass range is proportional to starting value. P. antarcticum and N. gibberifrons have the largest starting biomasses and the largest range of acceptable biomasses, likely due to their importance to predators diets. Demersal fish (shallow and deep groups) and toothfish, show very narrow ranges of biomass. Benthic groups were assigned a CV value of 1, as input biomass was based on region specific surveys. Results indicate benthic groups with higher biomasses also have larger ranges of acceptable input values. The largest ranges are for sponges and worms, which have the largest biomasses in surveys (Jazdzewski et al., 1986; Saiz-Salinas

443 J.3. Model Parameterization and Output et al., 1998; Piepenburg et al., 2002). Copepods have the largest range of biomass for zooplankton groups. While this is not surprising given it has the lowest CV at 0.4, compared to most groups with a value of 1. Juvenile krill and macro-zooplankton have the next largest ranges. Salps in comparison to other zooplankton have a narrow range of acceptable starting biomass indicating the model cannot support a large starting biomass of salps, although the ﬁtted model indicates higher biomasses are supported throughout the last 30 years. Results indicate the model can support higher biomasses of diatoms and ice algae, with lower biomasses of warmer water associated producers (cryptophytes and other producers).

Table J.11: Monte Carlo estimates using coeﬃcient of variation (CV) values based on pedigree ranking. Lower and Upper limits refer to 95% CI. All biomass values are represented in t · km−2

Functional Group Biomass Lower Mean Upper CV Limit Biomass Limit

1 Killer Whales 0.7 0.001 0.001 0.001 2 Leopard Seal 0.7 0.004 0.006 0.007 3 Ross Seal 0.4 0.002 0.004 0.006 4 Weddell Seal 0.7 0.015 0.021 0.027 5 Crabeater Seal 0.7 0.115 0.164 0.213 6 Antarctic Fur Seals 0.7 0.02 0.028 0.037 7 S Elephant Seals 0.7 0.005 0.006 0.008 8 Sperm whales 0.7 0.004 0.005 0.007 9 Blue Whales 0.7 0 0.001 0.001 10 Fin Whales 0.7 0.002 0.003 0.004 11 Minke whales 0.7 0.046 0.065 0.085 12 Humpback whales 0.7 0.014 0.02 0.026 13 Emperor penguins 0 0.001 0.005 0.009

Continued on Next Page

444 J.3. Model Parameterization and Output

Table J.11 Continued

Functional Group Biomass Lower Mean Upper CV Limit Biomass Limit 14 Gentoo Penguins 0.7 0.005 0.007 0.008 15 Chinstrap Penguins 0.7 0.004 0.005 0.007 16 Macaroni Penguin 0 0.003 0.014 0.024 17 Adelie Penguins 0.7 0.024 0.034 0.044 18 Flying birds 0.4 0.095 0.19 0.285 19 Cephalopods 0.4 1.245 2.49 3.735 20 Other Iceﬁsh 0.7 0.236 0.337 0.438 21 Toothﬁsh 0.7 0.032 0.046 0.06 22 Large Notothenioids 0.7 0.413 0.59 0.767 23 Small Notothenioids 0.7 0.239 0.341 0.443 24 Shallow Demersals 0.7 0.022 0.031 0.04 25 Deep demersals Lg 0.7 0.029 0.042 0.055 26 Deep demersals Sm 0.7 0.056 0.08 0.104 27 Myctophids 0.7 0.13 0.185 0.241 28 Other Pelagics 0.7 0.343 0.49 0.637 29 C. gunnari 0.7 0.203 0.29 0.377 30 P. antarcticum 0.7 0.875 1.25 1.625 31 N. gibberifrons 0.7 0.567 0.81 1.053 32 Mollusca 1 8.55 9.5 10.45 33 Salps 1 7.2 8 8.8 34 Urochordata 1 4.545 5.05 5.555 35 Porifera 1 11.447 12.719 13.991 36 Hemichordata 1 0.041 0.045 0.05 37 Brachiopoda 1 0.025 0.028 0.03 38 Bryozoa 1 0.442 0.491 0.54 39 Cnidarians 1 1.378 1.531 1.684 40 Crusteceans 1 3.252 3.613 3.974 41 Arthropod Other 1 0.909 1.01 1.111 42 Worms 1 10.8 12 13.2 43 Echinoidea 1 3.897 4.33 4.763

Continued on Next Page

445 J.3. Model Parameterization and Output

Table J.11 Continued

Functional Group Biomass Lower Mean Upper CV Limit Biomass Limit 44 Crinoidea 1 0.147 0.164 0.18 45 Ophiuroidea 1 6.084 6.76 7.436 46 Asteroidea 1 1.6 1.778 1.956 47 Holothuroidea 1 4.905 5.45 5.995 48 Krill Adult 1 8.172 9.08 9.988 49 Krill Juvenile 1 23.303 25.893 28.482 50 Krill Larvae 1 0.011 0.013 0.014 51 Krill Embryo 1 0.003 0.003 0.004 52 Macro-Zooplankton 0.7 5.719 8.17 10.621 53 Micro-Zooplankton 0.7 2.03 2.9 3.77 54 Cryptophytes 0.7 1.54 2.2 2.86 55 Copepods 0.4 7.6 15.2 22.8 56 Diatoms 0.7 12.187 17.41 22.633 57 Ice algae 0.7 17.5 25 32.5 58 Other Phytoplankton 0.4 2.75 5.5 8.25

Ecosim Output

Results for individual functional groups are presented as the average biomass over the last five years of the model fitting. Both fitting scenarios (A and B) are presented using either the SOI or SST (temp) for environmental forcing. Changes in environmental drivers are shown to have expected effects to the lowest trophic levels. Ice algae and diatoms are favored in colder years (Moline et al., 2000), and are expected to decline as sea ice decreases and temperatures warm. The ice algae and diatom groups show little differences between the fitted models, as they are driven with sea ice for both fitting attempts (figure J.8). As these groups are large contributors to detritus, the decline in these groups drives a decline in detritus. The other phytoplankton group increases under both scenarios, however cryptophytes only

446 J.3. Model Parameterization and Output

20 SST SOI 10 0 -10 -20 -30 -40 -50

Ending Ending Biomass Change (% from 1978)

Detritus

Diatoms

Ice algae Ice

Cryptophytes

OtherPhytopl.

Figure J.8: Model end biomass presented as percent change from the starting Ecopath biomass for producers and detrital groups. increase slightly under the SST fitted model. Under the SOI fitted scenario, salps have a higher biomass in the last few years of the model simulation causing increased predation on cryptophytes, thus reducing the biomass of this group. It should be noted that the SOI driver showed more extreme fluctuations from year to year, thereby causing larger changes for groups being forced with this driver. However, biomass trends for most species follow the same general pattern using different drivers, however the SOI fitted model shows more extreme annual variations. This variation in ending values for most groups is carried up the food web to higher trophic levels, most notably zooplankton. It is important to note the overall decline of detritus (of 32% and 35% for SST and SOI), which is an important factor to declines of benthic detritivores. Part of the detrital decline can be attributed to an overall decline in production (total production decreased by 31% and 34% for SST and SOI fitted models respectively. For the zooplankton groups in the model, salps are the only group to show increases of 32% and 45% for the SST and SOI fitted models (figure

447 J.3. Model Parameterization and Output

J.9). This most likely due to decreased competition, as other zooplankton groups show larger increases in the SOI scenario, salps have reduced competition for food, and the mediation function allowing salps to a have larger foraging area when sea ice decreases. This is the cause for the higher biomass for the last five years of the model run. While the temperature fitted model generally provides less extreme changes to functional groups, this is not the case for the salp group. Copepods decline is caused by declines in three of their 4 food sources (diatoms, ice algae and detritus). Krill are negatively impacted by declines in their food source (ice algae and diatoms), and a reduction in sea ice which decreases protection from predators. There were large reductions in krill biomass under both fitted models with the smallest declines to the adult group. While declines appear large when compared to starting values, the cumulative krill biomass for 1978 was 37.57t·km−2 with ending values of total krill biomass at 24.1 and 24.4t·km−2 for the SST and SOI fitted models, resulting in declines across all stages of 36% and 37% respectively. Biomass trends do show similar trends for both models (appendix P), but the SOI fitted model shows higher biomass peaks from 1990 onward. Decline of most benthic groups (figure J.10) is caused by decreases in detritus either as a food source causing declines, or by causing declines in other benthos which serve as prey items. For example the diet of worms was set to 60% detritus in the Ecopath model, therefore a reduction in this food source contributed to the decline of worms, which in turn contributes to the decline of other benthos. The Echinoidea group fares the best under both scenarios as predators such as worms and other arthropods decrease. The hemichordata groups shows the largest declines of 34% and 35% (for SST and SOI) due to the fact that the diet of this group is comprised completely of detritus. All fish groups show declines under both fitted models, with the exception of myctophids (figure J.11). Fishing mortality on all harvested groups is small in relation to predation mortality, indicating fishing is not causing the declines. However declines in biomass are driven by bottom up processes in the food web. While declines appear high, biomass trends show fluctuations

448 J.3. Model Parameterization and Output

40 SST SOI

-20

-40

-60

-80

Ending Ending Biomass Change (% from 1978)

Salps

Krill AdultKrill

Copepods

Krill LarvaeKrill

Krill Embryo Krill

Krill JuvenileKrill

Micro-Zoopl.

Cephalopods

Macro-Zoopl.

Figure J.9: Model end biomass presented as percent change from the starting biomass for zooplankton functional groups.

0 -5 -10 -15 -20 -25 -30 SST SOI -35 -40

Worms

Porifera

Bryozoa

Cnidaria

Ending Ending Biomass Change (% from 1978)

Mollusca

Crinoidea

Asteroidea

Echinoidea

Crusteceans

Ophiuroidea

Brachiopoda

Urochordata

Hemichordata

Holothuroidea

OtherArthropods

Figure J.10: Model end biomass presented as percent change from the starting biomass for benthic functional groups.

449 J.3. Model Parameterization and Output

10 0 10 20 30 40 SST SOI 50

Toothﬁsh

C. gunnari C.

Ending Ending Biomass Change (% from 1978)

Myctophids

OtherIceﬁsh

P. antarccum P.

OtherPelagics

N. gibberifronsN.

Lg. Nototheniidae Lg.

Sm. Nototheniidae Sm.

Shallow DemersalsShallow

Deepdemersals Lg.

Deepdemersals Sm.

Figure J.11: Model end biomass presented as percent change from the starting biomass for fish functional groups. throughout the simulations with the ending biomass at low values (appendix P). However, these lower biomass levels are shown to occur previously in the model simulation with most fish groups recovering to higher biomass in the mid 1990s to coincide with increases in krill. This suggests that fish populations should be able to respond to increased food conditions in the future. The myctophid biomass also shows peaks in 1988 and 1993 coinciding with peaks in juvenile krill and copepod biomasses. Although the biomass does drop off after 1999 it remains close to the starting value. Penguins and marine mammals show varying levels of declines for both fitted models (figures J.12 and J.13). Even though biomass accumulation rates were added for chinstrap and gentoo penguins, based on increases at PALMER station (Fraser, 2006), bottom up declines in the food web cause these and other groups to decline. Krill is an important component of the diet for all of these groups. Penguins, flying birds, and cephalopods show generally declining trends with two peaks in biomass in the early 1980s and late 1990s coinciding with changes in zooplankton groups. Emperor,

450 J.3. Model Parameterization and Output

0 -10 -20 -30 -40 SST SOI -50 -60

Ending Ending Biomass Change (% from 1978)

Flying birds

AdeliePenguins

Gentoo Penguins

Macaroni Penguin

Emperorpenguins

Chinstrap Penguins

Figure J.12: Model end biomass presented as percent change from the starting biomass for bird functional groups. chinstrap, gentoo and macaroni penguins show declines in biomass from 1984-1992 before increasing again in the late 1990s, and declining again in the early to mid 2000s. Adelie penguins also exhibit the same general trend, but biomass remains low longer, from 1984-1996, before a slight increase and then declines again in the early 2000s. Gentoo penguins at Cierva point (Gerlache Strait) showed increasing chick mortality from 1992/93 summer to 1995/95 summer with a high chick mortality in the 1995/96 summer (Quintana and Cirelli, 2000). While the authors did not link the higher chick mortality to declines in krill populations, this link has been shown for other land based krill predators such as fur seals, with krill being a likely cause for penguin declines. Crabeater and Antarctic fur seals show the largest declines in biomass of roughly 50% and 40% for each group (ﬁgure J.13). The diet of crabeater seals is dominated by krill, as their jaws are adapted for straining krill (Lowry et al., 1998). Antarctic fur seal pups show lower survival in years of low krill abundance, speciﬁcally years where larger sizes of krill are absent in the re-

451 J.3. Model Parameterization and Output

0 10 20 30 40

50 SST SOI 60

Ross SealRoss

Fin WhalesFin

Blue WhalesBlue

LeopardSeal

WeddellSeal

KillerWhales

Ending Ending Biomass Change (% from 1978)

Minke whales Minke

Sperm whalesSperm

CrabeaterSeal

Humpback whales

AntarccFur Seals

SouthernElephant Seals

Figure J.13: Model end biomass presented as percent change from the starting biomass for marine mammal functional groups gion, which is the preferred size of adult fur seals (Reid and Arnould, 1996). Female fur seals are dependent on local krill populations to feed while lactating (Boyd et al., 1998), reduced fur seal biomass at South Georgia in 1984 was linked to lower krill biomass in 1984, as females made longer foraging trips and higher pup mortality resulted (Costa et al., 1989). The biomass changes for Antarctic fur seals and elephant seals follow similar trends to penguins with peaks in the early 1980s and late 1990s (appendix P). How- ever for other pinniped species (leopard seals, Ross seals, Weddell seals and crabeater seals) the rebounding biomass trend for the late 1990s is much weaker. Cetacean species show general declines over the model simulations, with little to no indication of rebounding biomasses. Long term declines in krill have the potential to cause reproductive stress or aﬀect survival for baleen whales (Nicol et al., 2008). While issues such as reproductive stress are not incorporated into the current model, it is still important to note the declines that caused by bottom up forces within the model.

452 Appendix K

Antarctic Peninsula Model Vulnerabilities

453 Appendix K. Antarctic Peninsula Model Vulnerabilities

Table K.1: Vulnerabilities used for the ﬁtted Antarctic Peninsula model

Prey predator 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Killer Whales Leopard Seal 2 Ross Seal 2 2 Weddell Seal 2 2 Crabeater Seal 2 2 Antarctic Fur Seals 2 2 S. Elephant Seals 2 Sperm whales Blue Whales 2 Fin Whales 2 Minke whales 2 Humpback whales 2 Emperor penguins 2 Gentoo Penguins 2 2 Chinstrap Penguins 2 2 Macaroni Penguin 2 2 Adelie Penguins 2 1 Flying birds 2 2 Cephalopods 2 2 2 2 2 2 2 2 2 2 10 10 Other Iceﬁsh 2 2 2 2 2 2 2 2 Toothﬁsh 2 2 Large Nototh 2 2 2 2 2 2 2 2 Small Nototh 2 2 2 2 2 2 2 Shallow Demersals 2 2 2 Deep demersals Lg. 2 2 2 2 2 2 2 Deep demersals Sm. 2 2 2 2 2 2 2 Myctophids 2 2 2 2 2 2 2 2 2 2 Other Pelagics 2 2 2 2 2 2 2 2 2 2 C. gunnari 2 2 2 2 2 2 P. antarcticum 2 2 2 2 2 2 2 2 2 2 2 2 N. gibberifrons 2 2 2 2 2 2 Mollusca 2 2 2 2 2 Salps 2 2 2 1 1 Urochordata 1 2 Porifera 2 2 Hemichordata 2 2 2 Brachiopoda 2 2 2 Bryozoa 2 2 2 Cnidaria 2 2 2 2 Arth Crustecea 2 2 2 2 2 2 2 Arth Other 2 2 Worms 2 2 2 2 2 Echinoidea Crinoidea Ophiuroidea Asteroidea Holothuroidea 2 Krill Adult 2 2 2 2 2 2 2 2 2 2 2 2 10 10 Krill Juvenile 2 2 2 2 2 2 2 2 2 2 2 2 10 2 Krill Larvae Krill Embryo Macro-Zoopl. 2 2 2 2 2 2 2 Micro-Zoopl. 2 2 2 2 Cryptophytes 2 2 2 Copepods 2 2 2 2 Diatoms 2 2 2 Ice algae 2 2 Other Phytopl. 2 2 2 Detritus Table Continued on The Next Page

454 Appendix K. Antarctic Peninsula Model Vulnerabilities

Table K.1 Continued Prey predator 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Killer Whales Leopard Seal Ross Seal Weddell Seal Crabeater Seal Antarctic Fur Seals S. Elephant Seals Sperm whales Blue Whales Fin Whales Minke whales Humpback whales Emperor penguins Gentoo Penguins Chinstrap Penguins Macaroni Penguin Adelie Penguins 2 Flying birds 2 Cephalopods 2 2 2 2 2 2 2 2 2 Other Iceﬁsh 2 2 2 2 2 2 2 2 Toothﬁsh 2 2 2 2 Large Nototh 2 2 2 2 2 2 2 Small Nototh 2 2 2 2 2 2 2 2 2 Shallow Demersals 2 2 2 2 Deep demersals Lg. 2 2 2 2 Deep demersals Sm. 2 2 2 2 2 2 2 Myctophids 2 2 2 2 2 2 2 2 2 Other Pelagics 2 2 2 2 2 2 2 2 C. gunnari 2 2 2 2 2 P. antarcticum 2 2 2 2 2 2 2 2 2 2 N. gibberifrons 2 2 2 2 2 2 Mollusca 2 2 2 2 2 2 2 2 2 2 2 Salps 222222222222 Urochordata 2 2 2 2 Porifera Hemichordata 2 Brachiopoda 2 Bryozoa 2 2 2 Cnidaria 2 2 2 2 2 2 2 2 2 Arth Crustecea 22222222222222 Arth Other 2 2 2 2 2 2 2 2 Worms 22222222222 Echinoidea 2 Crinoidea 2 Ophiuroidea 2 Asteroidea 2 Holothuroidea 2 Krill Adult 21222222222222 Krill Juvenile 21212222222222 Krill Larvae 2 2 2 2 Krill Embryo 2 2 2 Macro-Zoopl. 2 2 2 2 2 2 2 2 2 2 Micro-Zoopl. 2 2 2 2 2 2 2 Cryptophytes Copepods 2 2 2 2 Diatoms Ice algae 2 Other Phytopl. 2 2 Detritus Table Continued on The Next Page

455 Appendix K. Antarctic Peninsula Model Vulnerabilities

Table K.1 Continued Prey predator 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Killer Whales Leopard Seal Ross Seal Weddell Seal Crabeater Seal Antarctic Fur Seals S. Elephant Seals Sperm whales Blue Whales Fin Whales Minke whales Humpback whales Emperor penguins Gentoo Penguins Chinstrap Penguins Macaroni Penguin Adelie Penguins Flying birds Cephalopods Other Iceﬁsh 2 Toothﬁsh Large Nototh Small Nototh 2 Shallow Demersals Deep demersals Lg. Deep demersals Sm. 2 Myctophids Other Pelagics 2 C. gunnari P. antarcticum 2 N. gibberifrons 2 Mollusca 2 2 2 2 2 Salps 2 2 2 2 100 Urochordata 2 2 2 2 Porifera 2 2 2 2 Hemichordata 2 Brachiopoda 2 Bryozoa 2 2 2 2 Cnidaria 2 2 2 2 2 2 Arth Crustecea 2 2 2 2 2 2 Arth Other 2 2 2 2 2 2 Worms 2 2 2 2 2 2 2 2 2 Echinoidea 2 2 2 Crinoidea 2 2 2 2 Ophiuroidea 2 2 2 2 Asteroidea 2 2 2 2 Holothuroidea 2 2 2 2 2 2 Krill Adult 2 2 2 Krill Juvenile 2 2 2 Krill Larvae 2 2 2 Krill Embryo 2 10 2 Macro-Zoopl. 2 2 2 2 2 2 2 2 2 2 Micro-Zoopl. 2 2 2 10 2 2 2 2 2 2 2 2 Cryptophytes 2 2 2 10 2 2 2 2 2 Copepods 2 2 2 2 2 2 2 2 2 2 2 Diatoms 2 2 2 2 2 2 2 2 2 2 2 Ice algae 2 2 2 2 2 2 2 2 2 2 2 Other Phytopl. 2 2 2 2 2 2 2 2 2 2 2 Detritus 2 2 2 2 2 2 2 2 2 2 2 Table Continued on The Next Page

456 Appendix K. Antarctic Peninsula Model Vulnerabilities

Table K.1 Continued Prey predator 44 45 46 47 48 49 50 51 52 53 55 Killer Whales Leopard Seal Ross Seal Weddell Seal Crabeater Seal Antarctic Fur Seals S. Elephant Seals Sperm whales Blue Whales Fin Whales Minke whales Humpback whales Emperor penguins Gentoo Penguins Chinstrap Penguins Macaroni Penguin Adelie Penguins Flying birds Cephalopods Other Iceﬁsh Toothﬁsh Large Nototh Small Nototh Shallow Demersals Deep demersals Lg. Deep demersals Sm. Myctophids Other Pelagics C. gunnari P. antarcticum N. gibberifrons Mollusca 2 2 Salps 2 Urochordata 2 Porifera 2 2 Hemichordata Brachiopoda Bryozoa 2 2 Cnidaria 2 Arth Crustecea 2 2 2 Arth Other Worms 2 2 2 Echinoidea Crinoidea Ophiuroidea 2 2 Asteroidea Holothuroidea Krill Adult 1 Krill Juvenile 2 1 Krill Larvae 1 Krill Embryo 1.3 Macro-Zoopl. 2 2 2 2 2 Micro-Zoopl. 2 2 2 2 2 3 Cryptophytes 2 10 4.6 2 1 3 Copepods 2 2 2 2 2 2 Diatoms 2 2 2 2 2 2 2 2 3 Ice algae 2 2 2 2 2 1 2 2 Other Phytopl. 2 2 2 2 100 2 1 3 3 Detritus 2 2 2 2 2 2 2 2 2 2

457 Appendix L

Antarctic Peninsula Model Mixed Trophic Impact Values

458 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1: Mixed trophic impacts for the Antarctic Peninsula Model

Impacting Impacted 1 2 3 4 5 6 7 8 9 1 Killer Whale -0.31 -0.62 -0.05 -0.34 0.095 0.288 0.575 -0.05 -0.71 2 Leopard seal -0.12 -0.01 -0.34 -0.12 -0.5 -0.64 -0.85 0.154 0.167 3 Ross seal 0.013 -0.01 -0.01 -0.01 0.002 0.001 0 -0.01 -0.01 4 Weddell Seal 0.107 -0.09 -0.04 -0.08 0.015 0.033 0.049 -0.04 -0.11 5 Crabeater Seal 0.103 -0.08 -0.04 -0.08 -0.06 -0.02 0.042 -0.04 -0.16 6 Antarctic fur seal 0.05 0 -0.06 -0.05 -0.02 -0.03 -0.04 -0.04 -0.05 7 S Elephant seal 0 0.007 -0.01 -0.01 0 -0.01 -0.03 -0.02 0.002 8 Sperm whale 0 0 0 0 0.001 0 -0.01 -0.01 0.001 9 Blue whale 0.001 0 0 0 0 0 0.001 0 0 10 fin whale 0.003 0 0 0 0 0.001 0.003 0 0 11 Minke whale 0.236 -0.22 -0.02 -0.12 0.029 0.096 0.199 -0.02 -0.25 12 Humpback whale 0.048 -0.05 0 -0.02 0.005 0.019 0.041 0 -0.05 13 Emperor penguin 0 0.012 -0.02 -0.01 -0.01 -0.01 -0.02 -0.01 0.003 14 Gentoo Penguin 0.005 0 -0.02 -0.01 0 -0.01 -0.02 -0.03 0 15 Chinstrap penguin 0.012 -0.01 -0.02 -0.01 0.001 0 -0.01 -0.03 -0.01 16 Macaroni Penguin 0.001 0.017 -0.02 -0.01 -0.01 -0.02 -0.03 -0.01 0 17 Adelie Penguin 0 0.065 -0.04 -0.03 -0.06 -0.07 -0.06 0.005 -0.02 18 Flying birds -0.01 -0.02 -0.05 -0.03 0.006 -0.02 -0.1 -0.12 0 19 Cephalopods 0.012 0.097 0.267 0.129 -0.08 0.048 0.481 0.617 -0.06 20 Other Icefish 0 0.012 -0.01 -0.02 0 -0.02 0.005 0 0.009 21 Toothfish 0 0 0 0 0.001 0 0.013 0.013 0 22 Lg Nototheniidae 0 0.001 0.029 0.012 -0.01 0.01 -0.01 0.001 -0.01 23 Sm Nototheniidae 0 0.007 0 0.001 -0.01 0 -0.01 0 0 24 Shallow demersals 0 0.01 0 0 -0.01 -0.01 -0.01 0.001 0.001 25 Deep demersal lg 0 0.001 0 0 0 0 0 0.003 0.001 26 Deep demersals sm 0 0.005 0 0 0 0 -0.01 0.018 0 27 Myctophids 0 0.006 0.011 0.004 -0.01 0.001 0.066 0 0.044 28 Other pelagics 0 -0.01 -0.05 -0.03 0.004 0 -0.08 -0.1 0.014 29 C. gunni 0 0.003 0.015 0.018 -0.02 0.01 -0.01 0 -0.01 30 P. antarcticum 0.014 -0.01 0.124 0.177 -0.01 -0.02 0.044 0.026 -0.02 31 N. gibberifron 0 0.003 0.013 0.012 -0.01 0.014 -0.01 -0.01 -0.01 32 Mollusca 0.021 -0.01 0.04 0.175 0.019 -0.01 0.094 0.018 -0.02 33 Salps 0 0.006 0.017 0.001 0.008 0.002 0 0.027 0.003 34 Urochordata 0 0.004 0.01 0.004 0 0.002 0.017 0.023 0 35 Porifera 0 0 0 0 0 0 0 0 0 36 Hemichordata 0 0 0 0 0 0 0 0.004 0 37 Brachiopoda 0 0 0 0 0 0 0 0.004 0 38 Bryozoa 0 0 0 0 0 0 0 0.004 0 39 Cnidaria 0.001 0.001 0.002 0 0.001 0.001 0 0.011 0.002 40 Crustaceans 0.007 0.015 0.047 0.074 -0.02 0.002 0.017 0.011 0 41 Arthropod other 0 0.001 0.006 -0.01 0 0.001 0.004 0.008 0 42 Worms 0.004 0.011 0.006 0 0 0.013 -0.01 0.011 0.011 43 Echinoidea 0 0 0 0 0.001 0 0 0 0 44 Crinoidea 0 0 0 0 0 0 0 0 0 45 Ophiuroidea -0.01 0.001 -0.01 -0.04 0 0 -0.02 -0.01 0.004 46 Asteroidea 0 0 0 0 0 0 0 0 0 47 Holothuroidea 0 0.001 0.001 0 0 0 0 0.004 0 48 Krill Adult 0.105 0.159 -0.09 -0.1 0.262 0.18 -0.15 0.033 0.263 49 Krill Sub-adult 0.102 0.052 0.003 -0.1 0.189 0.206 -0.01 0.101 0.158 50 Krill juvenile 0 0 0 0 0 0 0 0 0 51 Krill Larvae 0 0 0 0 0 0 0 0 0 52 Macro-zoopl. -0.05 -0.1 0.066 0.097 -0.16 -0.16 0.136 0 -0.21 53 Micro-zoopl. 0.017 -0.01 0.015 0 0 0.009 0.042 0.035 -0.02 54 Cryptophytes 0.005 -0.01 0.015 0.007 0 -0.01 0.023 0.022 -0.01 55 Copepods 0.022 0.03 -0.02 -0.01 0.05 0.041 -0.04 -0.02 0.069 56 Diatoms 0.037 0.016 0.005 0 0.052 0.042 0.01 0.024 0.051 57 Ice Algae 0.067 0.042 0.002 -0.03 0.116 0.097 0.002 0.061 0.096 58 Other phytopl. 0.01 -0.01 0.028 0.018 -0.01 -0.01 0.044 0.039 -0.02 59 Detritus 0.044 0.016 0.06 0.123 0.042 0.048 0.069 0.063 0.021 Krill Fishery 0 0 0.001 0.001 0 0 0.001 0 0 Other Fishery 0 0 0 0 0 0 0 0 0 Table Continued on Next Page

459 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1 Continued Impacting Impacted 10 11 12 13 14 15 16 17 18 1 Killer Whale -0.7 -0.7 -0.71 0.6 0.329 0.158 0.5 0.32 0 2 Leopard seal 0.162 0.139 0.175 -0.9 -0.66 -0.51 -0.82 -0.52 0.017 3 Ross seal -0.01 -0.01 -0.01 0.006 0 0 0.006 0.006 0 4 Weddell Seal -0.11 -0.1 -0.1 0.068 0.029 0.001 0.067 0.049 0 5 Crabeater Seal -0.16 -0.13 -0.17 0.022 0.009 -0.02 0.008 -0.03 -0.02 6 Antarctic fur seal -0.05 -0.05 -0.05 -0.01 -0.04 -0.04 -0.02 0 -0.01 7 S Elephant seal 0.002 0.002 0.003 -0.01 -0.01 -0.01 -0.01 0 0 8 Sperm whale 0.001 0 0.001 0 0 -0.01 0.001 0.002 0 9 Blue whale 0 0 0 0.001 0 0 0 0 0 10 fin whale 0 0 0 0.003 0.002 0.001 0.002 0.001 0 11 Minke whale -0.25 -0.24 -0.25 0.204 0.114 0.055 0.169 0.107 0 12 Humpback whale -0.05 -0.05 -0.05 0.041 0.023 0.011 0.034 0.021 0 13 Emperor penguin 0.003 0.003 0.003 -0.02 -0.02 -0.01 -0.01 -0.01 0 14 Gentoo Penguin 0 0 0 0 -0.03 -0.03 0 0.003 0 15 Chinstrap penguin -0.01 -0.01 -0.01 0.004 -0.02 -0.03 0.002 0.006 0 16 Macaroni Penguin 0 0 0 -0.02 -0.03 -0.02 -0.03 -0.01 0 17 Adelie Penguin -0.02 -0.01 -0.03 -0.09 -0.06 -0.05 -0.08 -0.08 -0.01 18 Flying birds 0 0.003 0 -0.01 -0.03 -0.04 -0.01 -0.17 -0.51 19 Cephalopods -0.05 -0.02 -0.04 -0.01 0.163 0.225 -0.03 -0.1 0.078 20 Other Icefish 0.008 0.005 0.008 -0.01 -0.04 -0.02 -0.01 0 0.007 21 Toothfish 0 0 0 0 0 0 0 0.002 0 22 Lg Nototheniidae -0.01 0 -0.01 0.017 0.023 0.019 0.031 -0.01 -0.01 23 Sm Nototheniidae 0 0 0 0.017 0.028 0.001 0.006 -0.02 0.005 24 Shallow demersals 0.001 0.002 0.002 -0.01 -0.01 -0.01 -0.01 0 0 25 Deep demersals lg 0.001 0 0.001 0 0.002 0.002 0 0 0 26 Deep demersals sm 0 0 0 -0.01 0.024 0.025 0 0 0 27 Myctophids 0.045 0 0.005 -0.01 -0.01 -0.01 0.009 -0.01 0.006 28 Other pelagics 0.014 0.008 0.004 -0.03 -0.03 -0.04 0.004 0.009 -0.01 29 C. gunni -0.01 -0.01 -0.01 -0.01 0 0.001 0.005 -0.02 0 30 P. antarcticum -0.01 -0.03 -0.04 0.185 0.009 0.018 -0.01 0.019 0 31 N. gibberifron -0.01 0 -0.01 -0.01 0.056 0.023 0.001 -0.01 -0.01 32 Mollusca -0.02 -0.03 -0.03 0.021 0.001 0 0.001 0.068 0.014 33 Salps 0 -0.01 0.004 0 0.018 0.019 0 -0.01 0.026 34 Urochordata 0 0 0 0 0.025 0.027 0 -0.01 0.003 35 Porifera 0 0 0 0 0.008 0.018 0 0 0 36 Hemichordata 0 0 0 0 0.019 0.019 0 0 0 37 Brachiopoda 0 0 0 0 0.014 0.014 0 0 0 38 Bryozoa 0 0 0 0 0.014 0.014 0 0 0 39 Cnidaria 0.001 0.001 0.002 0 0.025 0.025 0.001 0.001 0.001 40 Crustaceans 0 -0.01 -0.01 0.009 0.029 0.011 0.032 0.025 0.035 41 Arthropod other 0 0.002 0.001 0 -0.01 0 0 -0.01 0 42 Worms 0.008 0.003 0.011 0.009 0.003 0.002 0.011 0.003 -0.01 43 Echinoidea 0 0 0.001 0 -0.01 -0.01 0 0 0 44 Crinoidea 0 0 0 0 0 0 0 0 0 45 Ophiuroidea 0.003 0.005 0.007 -0.01 -0.01 -0.01 0 -0.02 -0.01 46 Asteroidea 0 0 0 0 0 0 0 0 0 47 Holothuroidea 0 0 0 0 0 0 0 0 0 48 Krill Adult 0.24 0.02 0.339 0.102 0.056 0.06 0.173 0.491 0.1 49 Krill Sub-adult 0.054 0.158 0.149 0.112 -0.1 -0.09 0.181 -0.11 0.068 50 Krill juvenile 0 0 0 0 0 0 0 0 0 51 Krill Larvae 0 0 0 0 0 0 0 0 0 52 Macro-zoopl. -0.13 -0.01 -0.2 -0.06 0.106 0.097 -0.13 -0.12 -0.07 53 Micro-zoopl. -0.01 0.054 -0.03 0.018 0.01 0.011 0.008 -0.01 0.002 54 Cryptophytes 0.009 0.014 -0.02 0.003 0.028 0.027 0 0 0.007 55 Copepods 0.057 0.004 0.082 0.039 -0.03 -0.03 0.043 0.073 0.034 56 Diatoms 0.073 0.044 0.054 0.047 0.021 0.02 0.045 0.067 0.034 57 Ice Algae 0.08 0.075 0.111 0.072 0.036 0.035 0.094 0.107 0.049 58 Other phytopl. 0.004 0.027 -0.03 0.008 0.051 0.048 0 -0.01 0.009 59 Detritus 0.001 0.018 0.009 0.049 0.036 0.03 0.064 0.031 0.041 Krill Fishery 0 0 0 0 0 0 0 0 0 Other Fishery 0 0 0 0 0 0 0 0 0 Table Continued on Next Page

460 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1 Continued Impacting Impacted 19 20 21 22 23 24 25 26 27 1 Killer Whale -0.06 -0.06 -0.11 -0.14 -0.01 -0.06 -0.08 -0.05 0 2 Leopard seal 0.183 0.134 0.136 0.327 0 0.044 0.223 0.129 0.009 3 Ross seal -0.02 -0.01 0.005 -0.02 0.003 0 0 0 0.001 4 Weddell Seal -0.06 -0.02 0.002 -0.09 0 0.014 -0.03 -0.02 -0.01 5 Crabeater Seal -0.04 -0.01 0.003 0 0.018 0.031 0 0.004 -0.03 6 Antarctic fur seal -0.06 -0.08 0.006 -0.12 0.004 -0.04 -0.03 0 0.013 7 S Elephant seal -0.02 0.008 -0.19 0.008 0.008 0 0.006 0.001 -0.02 8 Sperm whale -0.01 0.011 -0.1 0.004 0.005 0.008 -0.04 -0.01 0.004 9 Blue whale 0 0 0 0 0 0 0 0 0 10 fin whale 0 0 0 0 0 0 0 0 0 11 Minke whale -0.02 -0.02 -0.04 -0.05 0 -0.02 -0.03 -0.02 0.002 12 Humpback whale -0.01 0 -0.01 -0.01 0 0 -0.01 0 0 13 Emperor penguin -0.02 -0.01 0.007 -0.03 -0.01 0.007 0.009 0.008 0.009 14 Gentoo Penguin -0.03 -0.02 0.022 -0.05 -0.01 0.066 -0.18 -0.09 0.018 15 Chinstrap penguin -0.04 -0.01 0.031 -0.04 0.011 0.056 -0.18 -0.09 0.016 16 Macaroni Penguin -0.01 -0.05 -0.01 -0.14 -0.01 0.028 0.03 -0.02 0 17 Adelie Penguin 0.009 0.011 0.009 0.023 0.004 -0.35 0.009 0.008 -0.02 18 Flying birds -0.16 -0.13 0.054 0.014 -0.11 0.045 0.044 0 -0.08 19 Cephalopods -0.16 -0.05 -0.5 -0.09 -0.1 0.066 -0.07 0 -0.19 20 Other Icefish 0 -0.1 0.092 -0.11 -0.05 0.102 -0.16 -0.07 0.03 21 Toothfish 0 -0.07 -0.02 -0.01 -0.02 0 0 0 0 22 Lg Nototheniidae 0.005 0.022 0.033 -0.09 -0.09 -0.13 -0.1 -0.08 -0.09 23 Sm Nototheniidae -0.01 0.005 0.138 -0.01 -0.04 -0.05 0.022 0.04 -0.02 24 Shallow demersals 0.001 0 0 0 -0.01 -0.02 0.02 0 -0.01 25 Deep demersals lg 0 -0.01 -0.09 -0.03 0 -0.19 -0.01 -0.14 0.01 26 Deep demersals sm 0 -0.09 -0.02 0.008 -0.07 -0.14 0.069 -0.05 -0.03 27 Myctophids 0 -0.01 0 0.007 -0.02 -0.03 -0.01 0.026 -0.03 28 Other pelagics -0.12 -0.08 0.07 0.043 -0.05 0 -0.07 0.033 -0.1 29 C. gunni 0 0.027 0.051 -0.01 0 0.009 0.011 -0.01 -0.02 30 P. antarcticum 0.045 -0.04 -0.04 -0.03 -0.04 -0.05 0.04 -0.06 -0.03 31 N. gibberifron -0.01 0.144 0.079 -0.06 -0.04 -0.05 -0.04 -0.05 -0.01 32 Mollusca -0.01 -0.02 -0.03 0.001 0.038 -0.04 0.139 0.099 0.212 33 Salps 0.009 0.012 0.009 0.021 0 0.016 -0.01 0.009 0 34 Urochordata 0.032 0 -0.02 -0.01 0.003 0 0.009 0 -0.01 35 Porifera 0 0 0.001 0 0 0 -0.01 0 0 36 Hemichordata 0 0 0 0 0 0.001 0 0 0.001 37 Brachiopoda 0 0 0.001 0 0 0.002 -0.01 0 0 38 Bryozoa 0 0 0.001 0 0 0.003 -0.01 0.003 0 39 Cnidaria 0 0.019 0.006 -0.01 0.004 0 0.004 -0.01 0 40 Crustaceans 0.015 0.033 0.129 0.199 0.216 0.558 0.051 0.141 0.145 41 Arthropod other 0.017 0 -0.01 0.001 -0.04 -0.01 -0.02 -0.02 -0.02 42 Worms 0.014 0.016 0.027 0.036 0.128 0 0.021 0.042 -0.03 43 Echinoidea 0 0 0 -0.01 -0.01 -0.02 0.001 0 0 44 Crinoidea 0 0 0 0 0.001 0 0 0 0 45 Ophiuroidea 0 0 -0.01 -0.02 -0.04 -0.04 -0.03 -0.03 -0.05 46 Asteroidea 0 0 0 0 0 0 0 0 0 47 Holothuroidea 0.002 0.002 0.004 0.004 0.012 0.011 0 0.001 0 48 Krill Adult -0.03 0.091 0.098 0.053 0 -0.18 0.031 0.009 0.006 49 Krill Sub-adult 0.109 0.023 -0.08 -0.09 -0.19 -0.24 -0.03 -0.12 -0.31 50 Krill juvenile 0 0 0 0 0 0 0.01 0.003 0 51 Krill Larvae 0 0 0 0 0 0 0 0 0 52 Macro-zoopl. 0.036 0.004 -0.02 0.04 0.139 0.245 0.005 0.09 0.155 53 Micro-zoopl. 0.05 0.012 -0.03 -0.03 0.001 -0.01 0 -0.01 -0.03 54 Cryptophytes 0.021 0.011 -0.01 0.003 0.011 0.018 0 0.01 0.007 55 Copepods -0.03 0.003 0.031 -0.01 -0.01 -0.04 0 -0.01 0.13 56 Diatoms 0.019 0.023 0.006 -0.01 0.008 -0.02 0.006 0.004 0.091 57 Ice Algae 0.055 0.055 0 0.037 -0.02 -0.08 0.007 -0.01 -0.03 58 Other phytopl. 0.039 0.022 -0.01 0.043 0.015 0.017 0.023 0.007 0.015 59 Detritus 0.045 0.027 0.049 0.109 0.178 0.248 0.101 0.127 0.116 Krill Fishery 0 0 0 0 0 0.001 0 0 0 Other Fishery 0 0 0 0 0 0 0 0 0 Table Continued on Next Page

461 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1 Continued Impacting Impacted 28 29 30 31 32 33 34 35 36 1 Killer Whale -0.01 -0.09 0.023 -0.03 -0.01 0.007 0.01 -0.01 -0.2 2 Leopard seal 0.049 0.269 0 0.076 0.017 -0.02 -0.03 0.007 0.453 3 Ross seal 0 -0.01 0 0 0 0.002 0.004 0 0.001 4 Weddell Seal 0 -0.08 -0.05 -0.02 -0.01 0.011 0.013 -0.01 -0.02 5 Crabeater Seal -0.04 -0.05 -0.03 0.012 0 -0.02 0 0.008 0.027 6 Antarctic fur seal -0.02 -0.21 0.041 -0.05 0 0.011 0.014 0 0.037 7 S Elephant seal 0 0.007 0.01 0 0 0.003 0.006 0 0.009 8 Sperm whale 0 0.001 0.007 0 0 0.001 0.004 0 -0.02 9 Blue whale 0 0 0 0 0 0 0 0 0 10 fin whale 0 0 0 0 0 0 0 0 0 11 Minke whale -0.01 -0.04 0.007 -0.01 0.001 0.002 0.003 0.001 -0.06 12 Humpback whale 0 -0.01 0.002 0 0 0 0 0 -0.01 13 Emperor penguin 0.006 0.013 -0.02 0.015 0.003 0.004 0.004 0 0.01 14 Gentoo Penguin 0.006 -0.01 0.017 -0.06 0 0.004 0.003 0 -0.39 15 Chinstrap penguin 0.004 -0.01 0.018 -0.03 0 0.004 0.004 0 -0.37 16 Macaroni Penguin 0 -0.06 0.012 0.003 0.001 0.005 0.002 0 0.019 17 Adelie Penguin -0.04 0 -0.04 0.011 -0.01 0 -0.01 0 0.042 18 Flying birds -0.1 0.038 0.063 0.037 -0.01 -0.02 0.037 0.001 0.034 19 Cephalopods 0.036 -0.03 -0.37 0.072 0.091 -0.09 -0.21 0.018 -0.15 20 Other Icefish 0.026 -0.26 -0.02 -0.37 0.003 0.002 0.009 -0.01 0.03 21 Toothfish 0 0 0.002 0.018 0 0.001 0.001 0 0 22 Lg Nototheniidae -0.09 -0.1 -0.02 -0.06 0.005 -0.02 0.005 0.006 -0.01 23 Sm Nototheniidae 0.008 -0.01 -0.01 -0.03 -0.02 -0.01 -0.01 0.011 -0.01 24 Shallow demersals 0 0.003 0 -0.01 0 0 0 0.001 0.005 25 Deep demersals lg 0.009 -0.02 0 0.001 0 0.002 0 0 -0.05 26 Deep demersals sm -0.04 0.025 -0.01 0.036 0 0 0 0 -0.02 27 Myctophids 0.02 0.006 -0.01 -0.01 -0.05 -0.02 0 0 0.01 28 Other pelagics -0.07 0.026 -0.04 0.048 0 -0.01 0.03 0 0.034 29 C. gunni 0 -0.03 0.002 -0.02 0.005 -0.01 0 0.003 0.005 30 P. antarcticum -0.1 -0.03 -0.09 -0.2 -0.13 -0.05 -0.01 0.023 0.037 31 N. gibberifron 0.001 -0.06 0.001 -0.11 0.001 0 -0.01 0.014 -0.02 32 Mollusca 0.015 -0.03 0.093 -0.04 -0.05 0.068 0.034 0.055 -0.04 33 Salps 0.036 0.023 -0.01 0 0 -0.06 -0.03 0 -0.02 34 Urochordata 0.001 0 -0.02 0.009 0 -0.01 -0.02 0 -0.02 35 Porifera 0 0 0 0 -0.02 -0.01 -0.01 -0.03 -0.03 36 Hemichordata 0 0 0.001 0 0 0 0 0 -0.02 37 Brachiopoda 0.005 0 0 0 0 0 0 0 -0.01 38 Bryozoa 0.004 0 0 0 0 0 0 0 -0.01 39 Cnidaria 0.011 -0.01 0 0 0 -0.09 0.004 0 -0.03 40 Crustaceans 0.003 -0.04 0.051 0.263 -0.02 0.048 0.044 -0.04 -0.04 41 Arthropod other 0 0 -0.01 -0.02 -0.06 0.022 -0.2 0.027 0.008 42 Worms -0.01 0.006 0.002 0.081 -0.08 -0.39 -0.26 -0.42 0.048 43 Echinoidea 0 0.002 0 -0.01 0.005 0.017 0.01 -0.04 -0.1 44 Crinoidea 0 0 0 0 0 0.001 0 0.001 0.001 45 Ophiuroidea -0.01 0.009 -0.02 -0.02 -0.18 0.04 0.02 -0.13 0.005 46 Asteroidea 0 0 0 0 0.002 -0.01 -0.03 -0.01 0 47 Holothuroidea 0 0 0 0.014 -0.02 0 -0.03 -0.01 -0.01 48 Krill Adult 0.073 0.334 -0.08 0.03 0.019 0.031 0.044 0.043 -0.01 49 Krill Sub-adult 0.048 0.252 0.035 -0.12 -0.31 0.088 0.045 -0.31 -0.49 50 Krill juvenile 0 0 0 0.001 0 0 0 0 0 51 Krill Larvae 0 0 0 0 0 0.001 0 0 0 52 Macro-zoopl. -0.03 -0.29 0.033 0.069 0.103 -0.25 -0.16 0.05 0.149 53 Micro-zoopl. 0.012 0 0 -0.02 -0.02 -0.05 -0.07 -0.04 -0.03 54 Cryptophytes 0.016 -0.01 0 0.011 0.015 0.296 0.11 0.01 -0.01 55 Copepods -0.01 0.077 0.091 -0.03 -0.06 -0.09 -0.11 -0.01 0 56 Diatoms 0.013 0.055 0.067 -0.01 -0.02 0.023 0.071 -0.02 -0.05 57 Ice Algae 0.036 0.116 0.022 0.027 -0.05 0.007 0.108 -0.07 -0.16 58 Other phytopl. 0.019 -0.03 0.004 0.045 0.042 0.346 0.26 0.004 -0.04 59 Detritus 0.022 0.013 0.091 0.147 0.421 -0.11 -0.06 0.484 0.756 Krill Fishery 0 0 0 0 0 0 0 0 0.001 Other Fishery 0 0 0 0 0 0 0 0 0 Table Continued on Next Page

462 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1 Continued Impacting Impacted 37 38 39 40 41 42 43 44 45 1 Killer Whale -0.09 -0.02 0.003 0.017 0.018 0.003 0 -0.01 0 2 Leopard seal 0.197 0.041 -0.01 -0.06 -0.06 -0.01 0.001 0.016 0 3 Ross seal 0.001 0 0 0.002 0.004 0 0 0 0 4 Weddell Seal -0.01 -0.01 0.001 0.01 0.014 0 0 -0.01 0 5 Crabeater Seal 0.022 -0.01 0.021 0.03 0.018 0.014 0.005 0.005 0.006 6 Antarctic fur seal 0.022 -0.01 0.008 0.02 0.016 0.002 0 -0.01 0 7 S Elephant seal 0.006 0 0.001 0 0.005 0 0 0 0 8 Sperm whale -0.01 0 0 0 0.003 0 0 0 0 9 Blue whale 0 0 0 0 0 0 0 0 0 10 fin whale 0 0 0 0 0 0 0 0 0 11 Minke whale -0.03 -0.01 0.003 0.01 0.008 0.003 0.001 0 0.001 12 Humpback whale -0.01 0 0.001 0.002 0.002 0.001 0 0 0 13 Emperor penguin 0.003 0 0.001 0.004 0.006 0 0.001 0 0 14 Gentoo Penguin -0.18 -0.02 -0.01 0.01 0.007 0 0 0 0 15 Chinstrap penguin -0.18 -0.01 -0.01 0.004 0.008 0 0 -0.01 0 16 Macaroni Penguin 0.009 0 0.003 0.01 0.007 0.001 0.001 0 0.001 17 Adelie Penguin 0.034 0.001 0.011 0 0 0.004 0 0 0 18 Flying birds 0.053 0.002 0.003 -0.01 0.036 0.008 0 0.006 0 19 Cephalopods -0.09 0.012 -0.04 0.02 -0.17 -0.01 0.022 0.082 0.016 20 Other Icefish 0.002 -0.02 -0.03 0.055 0.002 0 0 0.004 0 21 Toothfish 0 0 0.002 0 0 0 0 0.002 0 22 Lg Nototheniidae 0.024 0.022 0.014 -0.05 -0.02 0.004 0 0.022 0 23 Sm Nototheniidae -0.01 0.03 0 -0.06 0.007 0 0 -0.13 0.001 24 Shallow demersals 0.003 0.008 0.005 -0.02 0 0.003 0 0.001 0 25 Deep demersals lg 0 0 -0.01 0.007 0.001 0 0 0 0 26 Deep demersals sm 0.002 0 0.004 -0.01 0 0 0 0.009 0 27 Myctophids 0 0.012 0.008 -0.04 0.001 0.013 -0.01 0.003 -0.01 28 Other pelagics -0.3 -0.02 -0.03 0 0.031 0 0 -0.01 0 29 C. gunni 0.002 0 0.003 0.008 0.003 0.002 0.003 0.003 0.002 30 P. antarcticum 0.049 0.031 0.023 -0.04 -0.06 0.049 -0.02 0.051 0 31 N. gibberifron -0.01 0.035 -0.01 -0.1 0 0 0 0.011 0.001 32 Mollusca -0.05 0.001 0.022 -0.04 0.081 -0.18 0.154 -0.03 0.103 33 Salps -0.03 -0.01 0.064 -0.01 0 0.01 -0.02 0 -0.01 34 Urochordata -0.01 -0.01 -0.01 0 0.037 0.001 0.004 -0.01 0 35 Porifera -0.02 -0.01 -0.01 0 0 0.012 0.032 -0.02 0.009 36 Hemichordata -0.01 0 0 0 0 0 0 0 0 37 Brachiopoda -0.01 0 0 0 0 0 0.002 0 0 38 Bryozoa -0.01 -0.01 0 0.003 0.008 0 0.008 0.119 0.002 39 Cnidaria -0.02 0 -0.02 0.001 0.008 -0.01 0.014 -0.01 0.005 40 Crustaceans -0.04 -0.27 -0.18 -0.16 -0.1 -0.1 0.104 0.001 0.041 41 Arthropod other -0.01 -0.1 -0.06 -0.03 -0.11 -0.07 0.056 -0.28 0.032 42 Worms 0.214 -0.03 -0.14 -0.02 -0.12 -0.11 -0.79 -0.07 -0.33 43 Echinoidea -0.27 -0.04 -0.04 -0.02 -0.06 -0.03 0.015 -0.17 0.001 44 Crinoidea 0 -0.07 0.001 0 0.001 0 0.002 -0.01 0 45 Ophiuroidea -0.01 -0.08 -0.1 -0.05 0.001 -0.06 0.05 0 -0.23 46 Asteroidea 0 0.003 0.004 0 0.026 -0.01 0.012 -0.01 -0.04 47 Holothuroidea -0.01 -0.02 -0.01 0.014 0.097 0 0.035 -0.05 0 48 Krill Adult -0.05 0.04 -0.14 -0.05 0.014 -0.01 -0.01 0.02 0.016 49 Krill Sub-adult -0.13 0.068 -0.1 -0.36 -0.21 -0.22 -0.13 -0.24 -0.19 50 Krill juvenile 0 0 0 0 0.004 0 0 0 0 51 Krill Larvae 0 0 0.001 0 0.001 0 0.001 0 0 52 Macro-zoopl. 0.084 -0.15 0.476 0.29 0.075 0.2 0.045 0.076 0.052 53 Micro-zoopl. -0.03 -0.06 0.055 -0.02 0.007 0.019 -0.02 -0.01 0.007 54 Cryptophytes 0.031 0.116 0.081 0.014 0.016 0.019 0.002 0.017 0.028 55 Copepods -0.08 -0.09 -0.12 -0.01 -0.02 -0.04 0.028 -0.02 -0.02 56 Diatoms -0.01 0.095 0.037 0 -0.01 0.003 0.026 -0.01 0.002 57 Ice Algae -0.02 0.123 0.113 -0.05 -0.04 -0.03 0.003 -0.04 -0.02 58 Other phytopl. 0.312 0.123 0.152 0.021 0.024 0.023 0.058 0.008 0.028 59 Detritus 0.144 -0.02 -0.17 0.363 0.206 0.256 0.12 0.368 0.254 Krill Fishery 0 0 0.001 0.001 0 0 0 0 0 Other Fishery 0 0 0 0 0 0 0 0 0 Table Continued on Next Page

463 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1 Continued Impacting Impacted 46 47 48 49 50 51 52 53 54 1 Killer Whale -0.01 -0.01 -0.04 0.002 0.004 0 0.011 0 0 2 Leopard seal 0.016 0.026 0.107 0.012 -0.01 -0.01 -0.04 0.003 0.005 3 Ross seal 0 0 0.001 0.002 0 0 0 0 0 4 Weddell Seal -0.01 -0.01 0.001 0.008 0 -0.01 0 0 0 5 Crabeater Seal 0.005 0.004 -0.11 -0.03 0.004 0.04 0.048 0.009 -0.01 6 Antarctic fur seal 0 -0.01 -0.01 0 0 -0.01 0.003 0 0 7 S Elephant seal 0 0 0.002 0.001 0 0 0 0 0 8 Sperm whale 0 0 0 0 0 0 0 0 0 9 Blue whale 0 0 0 0 0 0 0 0 0 10 fin whale 0 0 0 0 0 0 0 0 0 11 Minke whale 0 0 -0.02 -0.01 0.002 0.005 0.008 0.001 0 12 Humpback whale 0 0 -0.01 0 0.001 0.001 0.002 0 0 13 Emperor penguin 0 0 0 0.002 0 -0.01 0 0 0 14 Gentoo Penguin 0 -0.01 0 0.002 0.011 0 0 0 0 15 Chinstrap penguin 0 0 0 0.002 0.007 0 0 0 0 16 Macaroni Penguin 0 0 0 0 0.002 0 0.001 0 0 17 Adelie Penguin 0 0 -0.06 0.004 0.004 0.013 0.017 0 0 18 Flying birds -0.01 0 -0.01 -0.01 -0.02 -0.01 0.008 0.005 0 19 Cephalopods 0.075 0.059 -0.03 -0.03 0.129 0.12 0.022 0.022 0.002 20 Other Icefish 0 -0.02 0.007 0.005 0.031 -0.01 -0.01 0 0.001 21 Toothfish 0 0 0.001 0 0 0 0 0 0 22 Lg Nototheniidae 0.008 0.023 -0.01 0 -0.01 0.019 0.007 0.002 0 23 Sm Nototheniidae 0 0.016 -0.01 0 -0.03 0 0.006 0.001 0 24 Shallow demersals 0 0.006 0 0 0.001 0.001 0 0 0 25 Deep demersals lg 0 0 0.001 0.001 -0.03 0.001 0 0 0 26 Deep demersals sm 0 0.001 0 0.001 -0.04 -0.02 0 0 0 27 Myctophids -0.01 0.008 -0.01 0 0 0.006 0.004 0.002 0.001 28 Other pelagics -0.01 -0.01 -0.01 0.005 -0.02 -0.02 0.002 0 0 29 C. gunni 0.003 0.002 -0.02 -0.01 0.003 0.01 0.011 0.003 0 30 P. antarcticum 0.026 0.056 -0.01 -0.06 0.062 0.086 0.025 0.029 0 31 N. gibberifron 0.004 0.028 -0.01 -0.01 -0.04 0.01 0.01 0.001 0 32 Mollusca 0.066 0.038 -0.02 -0.02 -0.06 -0.09 0.02 -0.02 -0.01 33 Salps 0 0 0.003 0.006 0 0.001 -0.02 -0.13 -0.05 34 Urochordata 0 -0.01 0 0 -0.03 -0.02 0 -0.01 0 35 Porifera 0 -0.02 0.001 0 0.002 -0.02 0 0.001 0 36 Hemichordata 0 0 0 0 0 0 0 0 0 37 Brachiopoda 0 0 0 0 0 0 0 0 0 38 Bryozoa 0 0 0 0 -0.01 -0.01 0 0 0 39 Cnidaria 0 0 0.003 0.004 -0.01 -0.01 -0.01 0.011 0.008 40 Crustaceans 0.086 -0.19 -0.01 0 0.036 0.034 -0.02 0.002 0 41 Arthropod other -0.25 -0.18 0 0 -0.6 -0.5 0.007 0 0.001 42 Worms -0.47 -0.33 0.023 0.019 0.067 0.156 -0.08 0.051 0.044 43 Echinoidea -0.03 -0.02 0 0 0.041 -0.14 0.001 0 0 44 Crinoidea 0.001 0.001 0 0 0 0 0 0 0 45 Ophiuroidea 0.067 0.04 0.002 0.001 0.005 -0.01 0.003 -0.01 0 46 Asteroidea -0.01 -0.01 0 0 -0.02 -0.02 0 0.001 0.001 47 Holothuroidea -0.04 -0.04 0 0 -0.07 -0.07 0 0.001 0 48 Krill Adult 0.017 0.05 -0.03 -0.05 -0.12 -0.22 -0.29 0.07 0.012 49 Krill Sub-adult -0.24 -0.31 -0.12 -0.15 0.057 -0.4 -0.32 -0.33 0.093 50 Krill juvenile 0 0 0 0 -0.01 0 0 0 0 51 Krill Larvae 0 0 0 0 0 -0.01 0 0 0 52 Macro-zoopl. 0.076 0.045 -0.34 -0.38 -0.02 0.272 0.203 -0.1 -0.18 53 Micro-zoopl. 0.009 -0.02 -0.01 0.005 -0.08 -0.02 -0.04 -0.15 -0.39 54 Cryptophytes 0.009 0 -0.01 -0.03 0.012 0.007 0.092 0.11 -0.11 55 Copepods -0.01 0 0.162 0.046 -0.18 -0.06 -0.25 -0.15 -0.14 56 Diatoms 0 -0.03 0.127 0.037 -0.11 -0.06 0.036 0.053 0.155 57 Ice Algae -0.03 -0.09 0.207 0.166 0.601 -0.16 0.154 0.022 0.082 58 Other phytopl. 0.03 0 -0.03 -0.04 0.166 0 0.151 0.244 0.037 59 Detritus 0.336 0.522 -0.03 0.178 -0.19 0.673 -0.05 -0.02 0.095 Krill Fishery 0 0 -0.01 0 0.001 0.002 0.002 0 0 Other Fishery 0 0 0 0 0 0 0 0 0 Table Continued on Next Page

464 Appendix L. Antarctic Peninsula Model Mixed Trophic Impact Values

Table L.1 Continued Impacting Impacted 55 56 57 58 59 Krill Fishery Other Fishery 1 Killer Whale 0.006 0 0 0 0 -0.03 -0.05 2 Leopard seal -0.03 0.018 -0.01 0.007 -0.01 0.083 0.111 3 Ross seal 0 0.001 0 0 0 0.001 0 4 Weddell Seal -0.01 0.003 0 0.001 -0.01 0.003 -0.03 5 Crabeater Seal 0.04 -0.03 0.011 -0.01 0.024 -0.09 -0.01 6 Antarctic fur seal 0.003 0 0.001 0 0.002 -0.01 -0.05 7 S Elephant seal 0 0.001 0 0 0 0.002 -0.02 8 Sperm whale 0 0 0 0 0 0 -0.01 9 Blue whale 0 0 0 0 0 0 0 10 fin whale 0 0 0 0 0 0 0 11 Minke whale 0.007 -0.01 0.002 0 0.005 -0.02 -0.02 12 Humpback whale 0.002 0 0 0 0.001 -0.01 0 13 Emperor penguin 0 0 0 0 0 0 0 14 Gentoo Penguin 0 0.001 0 0 0 0 -0.01 15 Chinstrap penguin 0 0.001 0 0 0 0 0 16 Macaroni Penguin 0.001 0 0.001 0 0.001 0 -0.03 17 Adelie Penguin 0.009 -0.01 0 0 0 -0.04 0 18 Flying birds 0.005 0 0.002 0 0.005 -0.01 -0.02 19 Cephalopods 0.025 -0.02 0.009 -0.01 0.02 -0.03 -0.14 20 Other Icefish 0 0.002 0 0.001 0 0.007 0.027 21 Toothfish 0 0 0 0 0 0.001 0.1 22 Lg Nototheniidae 0.004 0 0.001 0 0.003 -0.01 0.058 23 Sm Nototheniidae 0.003 0 0.001 0 0.003 -0.01 0.115 24 Shallow demersals 0 0 0 0 0 0 0 25 Deep demersals lg 0 0 0 0 0 0.001 -0.02 26 Deep demersals sm 0 0 0 0 0 0 -0.02 27 Myctophids 0.002 0 0 0 0.002 -0.01 0.106 28 Other pelagics 0 0 0 0 0 -0.01 0.093 29 C. gunni 0.009 -0.01 0.003 0 0.006 -0.02 0.112 30 P. antarcticum 0.034 -0.02 0.018 -0.01 0.049 -0.02 0.046 31 N. gibberifron 0.006 0 0.002 0 0.006 -0.01 0.105 32 Mollusca 0.011 -0.01 0.005 -0.01 -0.02 -0.02 0.028 33 Salps 0 0.005 0.004 0 0 0.004 0.01 34 Urochordata 0 0 0 0 0 0 0 35 Porifera 0.001 0 0.001 0 -0.02 0 0 36 Hemichordata 0 0 0 0 0 0 0 37 Brachiopoda 0 0 0 0 0 0 0 38 Bryozoa 0 0 0 0 0 0 0.001 39 Cnidaria 0 0.002 0 0.002 0 0.004 0.003 40 Crustaceans 0.002 0 0.003 0 -0.01 -0.01 0.111 41 Arthropod other 0.001 0 0.002 0.002 0.01 0 -0.01 42 Worms -0.01 0.008 0 0.017 -0.04 0.022 0.029 43 Echinoidea 0 0 0 0 0.002 0 0 44 Crinoidea 0 0 0 0 0 0 0 45 Ophiuroidea 0 0.001 0 0.002 0.002 0.002 -0.02 46 Asteroidea 0 0 0 0 0 0 0 47 Holothuroidea 0.001 0 0.001 0 -0.01 0 0.004 48 Krill Adult -0.17 0.118 0 0.039 0.034 0.714 0.067 49 Krill Sub-adult -0.42 0.229 -0.23 0.112 -0.58 0.121 -0.05 50 Krill juvenile 0 0 0 0 0 0 0 51 Krill Larvae 0 0 0 0 0 0 0 52 Macro-zoopl. 0.241 -0.16 0.06 -0.12 0.237 -0.35 0.012 53 Micro-zoopl. -0.02 -0.01 -0.02 -0.23 -0.02 -0.01 -0.01 54 Cryptophytes 0.01 -0.01 0.001 -0.06 0.014 -0.01 0.004 55 Copepods -0.43 -0.43 -0.19 -0.09 -0.02 0.133 0.031 56 Diatoms 0.381 -0.3 -0.15 -0.14 -0.03 0.104 0.027 57 Ice Algae 0.01 -0.04 -0.12 -0.07 -0.13 0.197 0.027 58 Other phytopl. 0.042 -0.04 -0.01 -0.12 0.014 -0.03 0.013 59 Detritus -0.09 0.047 -0.05 0.01 0 0.02 0.084 Krill Fishery 0.001 0 0 0 0 0 0 Other Fishery 0 0 0 0 0 0 0

465 Appendix M

Antarctic Peninsula Monte Carlo CV Values

466 Appendix M. Antarctic Peninsula Monte Carlo CV Values

Table M.1: Monte Carlo CV values used for the Antarctic Peninsula model

Group name Biomass P/B Q/B Diet 1 Killer Whales 0.7 0.5 0.5 1 2 Leopard Seal 0.7 0.5 0.5 1 3 Ross Seal 0.4 0.5 0.5 0.2 4 Weddell Seal 0.7 0.5 0.5 1 5 Crabeater Seal 0.7 0.5 0.5 1 6 Antarctic Fur Seals 0.7 0.5 0.5 1 7 S Elephant Seals 0.7 0.5 0.5 0.7 8 Sperm whales 0.7 0.5 0.5 0.5 9 Blue Whales 0.7 0.5 0.5 0.7 10 Fin Whales 0.7 0.5 0.5 0.7 11 Minke whales 0.7 0.5 0.5 0.7 12 Humpback whales 0.7 0.5 0.5 0.7 13 Emperor penguins 0 0.7 0.5 0.7 14 Gentoo Penguins 0.7 0.5 0.5 0.7 15 Chinstrap Penguins 0.7 0.5 0.5 0.7 16 Macaroni Penguin 0 0.5 0.5 0.5 17 Adelie Penguins 0.7 1 0.5 0.7 18 Flying birds 0.4 0.8 0.8 0.2 19 Cephalopods 0.4 0.2 0.2 0.2 20 Other Iceﬁsh 0.7 0.5 0.5 0.5 21 Toothﬁsh 0.7 0.5 0.5 0.5 22 Large Nototheniidae 0.7 0.5 0.5 0.5 23 Small Nototheniidae 0.7 0.5 0.5 0.5 24 Shallow Demersals 0.7 0.5 0.5 0.5 25 Deep demersals Large 0.7 0.5 0.5 1 26 Deep demersals Small 0.7 0.5 0.5 1 27 Myctophids 0.7 0.5 0.5 0.5 28 Other Pelagics 0.7 0.5 0.5 0.5 29 C. gunnari 0.7 0.5 0.5 0.5 30 P. antarcticum 0.7 0.5 0.5 0.5 31 N. gibberifrons 0.7 0.5 0.5 0.5 32 Mollusca 1 0.2 0.2 0.5 33 Salps 1 0.8 0.8 0.5 34 Urochordata 1 0.2 0.2 0.5 35 Porifera 1 0.2 0.2 0.5 36 Hemichordata 1 0.2 0.2 0.5 37 Brachiopoda 1 0.2 0.2 0.5 38 Bryozoa 1 0.2 0.2 0.5 39 Cnidaria 1 0.2 0.2 0.5 40 Crusteceans 1 0.2 0.2 0.5 41 Other Arthropods 1 0.2 0.2 0.5 42 Worms 1 0.2 0.2 0.5 43 Echinoidea 1 0.2 0.2 0.5 44 Crinoidea 1 0.2 0.2 0.5 45 Ophiuroidea 1 0.2 0.2 0.5 46 Asteroidea 1 0.2 0.2 0.5 47 Holothuroidea 1 0.2 0.2 0.5 48 Krill Adult 1 0.8 1 1 49 Krill Juvenile 1 0.8 0.5 1 50 Krill Larvae 1 0.8 0.5 1 51 Krill Embryo 1 0.8 0.5 1 52 Macro-Zoopl. 0.7 0 0 0.2 53 Micro-Zoopl. 0.7 0 0 0.2 54 Cryptophytes 0.7 - - - 55 Copepods 0.4 0.6 0.6 0.7 56 Diatoms 0.7 - - - 57 Ice algae 0.7 - - - 58 Other Phytoplankton 0.4 - - -

467 Appendix N

Antarctic Peninsula Monte Carlo Results

468 Appendix N. Antarctic Peninsula Monte Carlo Results

Table N.1: Monte Carlo results for Biomass (t · km−2), P/B (y−1), Ecotrophic Eﬃciency (EE), and Biomass Accumulation (t · km−2 · y−1)

Biomass P/B Group name Lower Mean Upper Lower Mean Upper 1 Killer Whales 0.001 0.001 0.001 0.025 0.050 0.075 2 Leopard Seal 0.004 0.006 0.007 0.060 0.120 0.180 3 Ross Seal 0.002 0.004 0.006 0.065 0.130 0.195 4 Weddell Seal 0.015 0.021 0.027 0.085 0.170 0.255 5 Crabeater Seal 0.115 0.164 0.213 0.045 0.090 0.135 6 Antarctic Fur Seals 0.020 0.028 0.037 0.088 0.175 0.263 7 S Elephant Seals 0.005 0.006 0.008 0.083 0.165 0.248 8 Sperm whales 0.004 0.005 0.007 0.017 0.034 0.051 9 Blue Whales 0.000 0.001 0.001 0.016 0.032 0.048 10 Fin Whales 0.002 0.003 0.004 0.018 0.035 0.053 11 Minke whales 0.046 0.065 0.085 0.032 0.064 0.096 12 Humpback whales 0.014 0.020 0.026 0.020 0.040 0.060 13 Emperor penguins 0.001 0.005 0.009 0.105 0.150 0.195 14 Gentoo Penguins 0.005 0.007 0.008 0.110 0.220 0.330 15 Chinstrap Penguins 0.004 0.005 0.007 0.165 0.330 0.495 16 Macaroni Penguin 0.003 0.014 0.024 0.150 0.300 0.450 17 Adelie Penguins 0.024 0.034 0.044 0.261 0.290 0.319 18 Flying birds 0.095 0.190 0.285 0.272 0.340 0.408 19 Cephalopods 1.245 2.490 3.735 0.380 0.950 1.520 20 Other Iceﬁsh 0.236 0.337 0.438 0.190 0.380 0.570 21 Toothﬁsh 0.032 0.046 0.060 0.083 0.165 0.248 22 Large Nototheniidae 0.413 0.590 0.767 0.185 0.370 0.555 23 Small Nototheniidae 0.239 0.341 0.443 0.325 0.650 0.975 24 Shallow Demersals 0.022 0.031 0.040 0.375 0.750 1.125 25 Deep demersals Lg 0.029 0.042 0.055 0.145 0.290 0.435 26 Deep demersals Sm 0.056 0.080 0.104 0.325 0.650 0.975 27 Myctophids 0.130 0.185 0.241 0.675 1.350 2.025 28 Other Pelagics 0.343 0.490 0.637 0.275 0.550 0.825 29 C. gunnari 0.203 0.290 0.377 0.240 0.480 0.720 30 P. antarcticum 0.875 1.250 1.625 0.550 1.100 1.650 31 N. gibberifrons 0.567 0.810 1.053 0.205 0.410 0.615 32 Mollusca 8.550 9.500 10.450 0.256 0.639 1.022 33 Salps 2.250 2.500 2.750 2.400 3.000 3.600 34 Urochordata 4.545 5.050 5.555 0.094 0.234 0.374 35 Porifera 11.447 12.719 13.991 0.064 0.159 0.254 36 Hemichordata 0.041 0.045 0.050 0.150 0.375 0.600 37 Brachiopoda 0.025 0.028 0.030 0.359 0.898 1.437 38 Bryozoa 0.442 0.491 0.540 0.190 0.475 0.760 39 Cnidaria 1.378 1.531 1.684 0.100 0.250 0.400 40 Crusteceans 3.252 3.613 3.974 0.420 1.050 1.680 41 Arthropod Other 0.909 1.010 1.111 0.246 0.616 0.985 42 Worms 10.800 12.000 13.200 0.280 0.700 1.120 43 Echinoidea 3.897 4.330 4.763 0.046 0.116 0.186 44 Crinoidea 0.147 0.164 0.180 0.050 0.125 0.200 45 Ophiuroidea 6.084 6.760 7.436 0.180 0.450 0.720 46 Asteroidea 1.600 1.778 1.956 0.092 0.231 0.370 47 Holothuroidea 4.905 5.450 5.995 0.126 0.316 0.505 48 Krill Adult 8.172 9.080 9.988 1.200 1.500 1.800 49 Krill Juvenile 23.303 25.893 28.482 0.720 0.900 1.080 50 Krill Larvae 0.011 0.013 0.014 2.000 2.500 3.000 51 Krill Embryo 0.003 0.003 0.004 6.400 8.000 9.600 52 Macro-Zooplankton 5.719 8.170 10.621 1.615 8.073 14.531 53 Micro-Zooplankton 1.456 2.080 2.704 5.400 27.000 48.600 54 Cryptophytes 1.260 1.800 2.340 60.000 75.000 90.000 55 Copepods 10.940 21.880 32.820 10.306 17.177 24.048 56 Diatoms 12.187 17.410 22.633 72.408 90.510 108.612 57 Ice algae 17.500 25.000 32.500 36.000 45.000 54.000 58 Other Phytoplankton 2.750 5.500 8.250 84.000 105.000 126.000 Table Continued on Next Page

469 Appendix N. Antarctic Peninsula Monte Carlo Results

Table N.1 Continued Biomass P/B EE BA Group name Lower Mean Upper Lower Mean Upper 1 Killer Whales 0.000 0.000 0.000 0.000 0.000 0.000 2 Leopard Seal 0.509 0.637 0.764 -0.001 0.000 0.001 3 Ross Seal 0.664 0.830 0.996 0.000 0.000 0.000 4 Weddell Seal 0.551 0.689 0.827 -0.002 0.000 0.002 5 Crabeater Seal 0.290 0.363 0.435 -0.016 0.000 0.016 6 Antarctic Fur Seals 0.690 0.862 1.000 -0.003 0.000 0.003 7 S Elephant Seals 0.349 0.437 0.524 -0.001 0.000 0.001 8 Sperm whales 0.000 0.000 0.000 -0.001 0.000 0.001 9 Blue Whales 0.546 0.683 0.820 0.000 0.000 0.000 10 Fin Whales 0.419 0.524 0.629 0.000 0.000 0.000 11 Minke whales 0.728 0.910 1.000 -0.007 0.000 0.007 12 Humpback whales 0.770 0.963 1.000 -0.002 0.000 0.002 13 Emperor penguins 0.746 0.933 1.000 -0.001 0.000 0.001 14 Gentoo Penguins 0.514 0.642 0.771 0.000 0.000 0.001 15 Chinstrap Penguins 0.556 0.696 0.835 0.000 0.001 0.001 16 Macaroni Penguin 0.298 0.373 0.447 -0.001 0.000 0.001 17 Adelie Penguins 0.635 0.793 0.952 -0.003 0.000 0.003 18 Flying birds 0.760 0.950 1.000 -0.019 0.000 0.019 19 Cephalopods 0.522 0.653 0.784 -0.249 0.000 0.249 20 Other Iceﬁsh 0.581 0.726 0.871 -0.034 0.000 0.034 21 Toothﬁsh 0.502 0.627 0.752 -0.005 0.000 0.005 22 Large Nototheniidae 0.362 0.452 0.543 -0.059 0.000 0.059 23 Small Nototheniidae 0.699 0.873 1.000 -0.034 0.000 0.034 24 Shallow Demersals 0.290 0.362 0.434 -0.003 0.000 0.003 25 Deep demersals Lg 0.642 0.803 0.964 -0.004 0.000 0.004 26 Deep demersals Sm 0.656 0.820 0.984 -0.008 0.000 0.008 27 Myctophids 0.706 0.883 1.000 -0.019 0.000 0.019 28 Other Pelagics 0.670 0.838 1.000 -0.049 0.000 0.049 29 C. gunnari 0.380 0.475 0.571 -0.029 0.000 0.029 30 P. antarcticum 0.483 0.603 0.724 -0.125 0.000 0.125 31 N. gibberifrons 0.516 0.645 0.774 -0.081 0.000 0.081 32 Mollusca 0.436 0.545 0.654 -0.950 0.000 0.950 33 Salps 0.167 0.209 0.251 -0.250 0.000 0.250 34 Urochordata 0.443 0.554 0.665 -0.505 0.000 0.505 35 Porifera 0.652 0.815 0.979 -1.272 0.000 1.272 36 Hemichordata 0.428 0.534 0.641 -0.005 0.000 0.005 37 Brachiopoda 0.472 0.590 0.708 -0.003 0.000 0.003 38 Bryozoa 0.760 0.950 1.000 -0.049 0.000 0.049 39 Cnidaria 0.786 0.982 1.000 -0.153 0.000 0.153 40 Crusteceans 0.711 0.888 1.000 -0.361 0.000 0.361 41 Arthropod Other 0.784 0.981 1.000 -0.101 0.000 0.101 42 Worms 0.675 0.844 1.000 -1.200 0.000 1.200 43 Echinoidea 0.619 0.774 0.929 -0.433 0.000 0.433 44 Crinoidea 0.419 0.523 0.628 -0.016 0.000 0.016 45 Ophiuroidea 0.441 0.551 0.661 -0.676 0.000 0.676 46 Asteroidea 0.619 0.774 0.928 -0.178 0.000 0.178 47 Holothuroidea 0.750 0.938 1.000 -0.545 0.000 0.545 48 Krill Adult 0.554 0.693 0.831 -0.908 0.000 0.908 49 Krill Juvenile 0.634 0.792 0.950 -2.589 0.000 2.589 50 Krill Larvae 0.636 0.795 0.954 -0.001 0.000 0.001 51 Krill Embryo 0.369 0.461 0.554 0.000 0.000 0.000 52 Macro-Zooplankton 0.760 0.950 1.000 -0.817 0.000 0.817 53 Micro-Zooplankton 0.745 0.931 1.000 -0.208 0.000 0.208 54 Cryptophytes 0.560 0.700 0.840 -0.180 0.000 0.180 55 Copepods 0.760 0.950 1.000 -2.188 0.000 2.188 56 Diatoms 0.397 0.497 0.596 -1.741 0.000 1.741 57 Ice algae 0.735 0.919 1.000 -2.500 0.000 2.500 58 Other Phytoplankton 0.660 0.825 0.989 -0.550 0.000 0.550

470 Appendix O

Antarctic Peninsula Monte Carlo Graphs

471 Appendix O. Antarctic Peninsula Monte Carlo Graphs

Marine Mammals and Birds Fish

0.30 2.00

1.75 0.25

1.50 0.20 1.25

0.15 1.00

0.75 Biomass (t/km2) Biomass 0.10 (t/km2) Biomass

0.50 0.05 0.25

Invertebrates Plankton Groups

15 40

12 30

25 9

6 15 Biomass (t/km2) Biomass Biomass (t/km2) Biomass

10 3 5

0 0 Salps Diatoms Worms Ice algae Porifera Bryozoa Mollusca Cnidaria Krill Adult Copepods Crinoidea Asteroidea Echinoidea Krill Larvae Krill Embryo Cephalopods Krill Juvenile Ophiuroidea Urochordata Cryptophytes Brachiopoda Crusteceans Hemichordata Holothuroidea Arthropod Other Micro−Zooplankton Macro−Zooplankton Other Phytoplankton Figure O.1: Mean and 95% CI for Monte Carlo biomass results as presented in t · km−2 from 1000 simulations. CV values presented in appendix M.

472 Appendix P

Antarctic Peninsula Model Biomass Trends By Species

473 Appendix P. Antarctic Peninsula Model Biomass Trends By Species

3.0 Killer Whales Leopard Seal Ross Seal Weddell Seal Crabeater Seal 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Ant Fur Seals S Elephant Seals Sperm Whales Blue Whales Fin Whales 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Minke Whales Humpback Whales Emperor P Gentoo P Chinstrap P 2.5 2.0

Relative Biomass Relative 1.5 1.0 0.5 0.0

3.0 Macaroni P Adelie P Flying birds Cephalopods Other Icefish 2.5 2.0 1.5 1.0 0.5 0.0

1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Year

Figure P.1: Biomass trends for the SST (solid lines) and SOI (dotted lines) ﬁtted models.

474 Appendix P. Antarctic Peninsula Model Biomass Trends By Species

3.0 Toothfish Lg Nototheniidae Sm Nototheniidae Shallow Demersals Deep demersals Lg 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Deep demersals Sm Myctophids Other Pelagics C. gunnari P. antarcticum 2.5 2.0 1.5 1.0 0.5 0.0

3.0 N. gibberifrons Mollusca Salps Urochordata Porifera 2.5 2.0

Relative Biomass Relative 1.5 1.0 0.5 0.0

3.0 Hemichordata Brachiopoda Bryozoa Cnidaria Crustecea 2.5 2.0 1.5 1.0 0.5 0.0

1980 1995 1980 1995 1980 1995 1980 1995 1980 1995 Year

Biomass trends for the SST (solid lines) and SOI (dotted lines) ﬁtted models.

475 Appendix P. Antarctic Peninsula Model Biomass Trends By Species

3.0 Other Arthropod Worms Echinoidea Crinoidea Ophiuroidea 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Asteroidea Holothuroidea Krill Adult Krill Juvenile Krill Larvae 2.5 2.0 1.5 1.0 0.5 0.0

3.0 Krill Embryo Macro−Zoopl Micro−Zoopl Cryptophytes Copepods 2.5 2.0

Relative Biomass Relative 1.5 1.0 0.5 0.0

3.0 Diatoms Ice algae Other Phytopl Detritus 2.5 2.0 1.5 1.0 0.5 0.0

1980 1995 1980 1995 1980 1995 1980 1995 Year

Biomass trends for the SST (solid lines) and SOI (dotted lines) ﬁtted models.

476